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Hernández-Mustieles MA, Lima-Carmona YE, Pacheco-Ramírez MA, Mendoza-Armenta AA, Romero-Gómez JE, Cruz-Gómez CF, Rodríguez-Alvarado DC, Arceo A, Cruz-Garza JG, Ramírez-Moreno MA, Lozoya-Santos JDJ. Wearable Biosensor Technology in Education: A Systematic Review. Sensors (Basel) 2024; 24:2437. [PMID: 38676053 PMCID: PMC11054421 DOI: 10.3390/s24082437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Revised: 03/31/2024] [Accepted: 04/02/2024] [Indexed: 04/28/2024]
Abstract
Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.
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Affiliation(s)
- María A. Hernández-Mustieles
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Yoshua E. Lima-Carmona
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Maxine A. Pacheco-Ramírez
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Axel A. Mendoza-Armenta
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - José Esteban Romero-Gómez
- Mechatronics Department, School of Engineering and Sciences, Guadalajara Campus, Tecnologico de Monterrey, Guadalajara 45201, Mexico;
| | - César F. Cruz-Gómez
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Diana C. Rodríguez-Alvarado
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Alejandro Arceo
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Jesús G. Cruz-Garza
- Department of Neurosurgery, Houston Methodist Research Institute, Houston, TX 77030, USA;
| | - Mauricio A. Ramírez-Moreno
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
| | - Jorge de J. Lozoya-Santos
- Mechatronics Department, School of Engineering and Sciences, Monterrey Campus, Tecnologico de Monterrey, Monterrey 64700, Mexico; (M.A.H.-M.); (Y.E.L.-C.); (M.A.P.-R.); (A.A.M.-A.); (C.F.C.-G.); (D.C.R.-A.); (A.A.); (M.A.R.-M.)
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Scheer T, Rohde M, Breithaupt R, Jung N, Lange R. Customizable Presentation Attack Detection for Improved Resilience of Biometric Applications Using Near-Infrared Skin Detection. Sensors (Basel) 2024; 24:2389. [PMID: 38676006 PMCID: PMC11053657 DOI: 10.3390/s24082389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/28/2024] [Accepted: 04/03/2024] [Indexed: 04/28/2024]
Abstract
Due to their user-friendliness and reliability, biometric systems have taken a central role in everyday digital identity management for all kinds of private, financial and governmental applications with increasing security requirements. A central security aspect of unsupervised biometric authentication systems is the presentation attack detection (PAD) mechanism, which defines the robustness to fake or altered biometric features. Artifacts like photos, artificial fingers, face masks and fake iris contact lenses are a general security threat for all biometric modalities. The Biometric Evaluation Center of the Institute of Safety and Security Research (ISF) at the University of Applied Sciences Bonn-Rhein-Sieg has specialized in the development of a near-infrared (NIR)-based contact-less detection technology that can distinguish between human skin and most artifact materials. This technology is highly adaptable and has already been successfully integrated into fingerprint scanners, face recognition devices and hand vein scanners. In this work, we introduce a cutting-edge, miniaturized near-infrared presentation attack detection (NIR-PAD) device. It includes an innovative signal processing chain and an integrated distance measurement feature to boost both reliability and resilience. We detail the device's modular configuration and conceptual decisions, highlighting its suitability as a versatile platform for sensor fusion and seamless integration into future biometric systems. This paper elucidates the technological foundations and conceptual framework of the NIR-PAD reference platform, alongside an exploration of its potential applications and prospective enhancements.
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Affiliation(s)
- Tobias Scheer
- Institute of Safety and Security Research, University of Applied Sciences Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany; (M.R.); (N.J.); (R.L.)
| | - Markus Rohde
- Institute of Safety and Security Research, University of Applied Sciences Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany; (M.R.); (N.J.); (R.L.)
| | - Ralph Breithaupt
- Federal Office for Information Security, Godesberger Allee 185-189, 53175 Bonn, Germany;
| | - Norbert Jung
- Institute of Safety and Security Research, University of Applied Sciences Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany; (M.R.); (N.J.); (R.L.)
| | - Robert Lange
- Institute of Safety and Security Research, University of Applied Sciences Bonn-Rhein-Sieg, Grantham-Allee 20, 53757 Sankt Augustin, Germany; (M.R.); (N.J.); (R.L.)
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3
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Kordas A, Bartuzi-Trokielewicz E, Ołowski M, Trokielewicz M. Synthetic Iris Images: A Comparative Analysis between Cartesian and Polar Representation. Sensors (Basel) 2024; 24:2269. [PMID: 38610479 PMCID: PMC11014044 DOI: 10.3390/s24072269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/09/2024] [Accepted: 03/28/2024] [Indexed: 04/14/2024]
Abstract
In recent years, the advancement of generative techniques, particularly generative adversarial networks (GANs), has opened new possibilities for generating synthetic biometric data from different modalities, including-among others-images of irises, fingerprints, or faces in different representations. This study presents the process of generating synthetic images of human irises, using the recent StyleGAN3 model. The novelty presented in this work consists in producing generated content in both Cartesian and polar coordinate representations, typically used in iris recognition pipelines, such as the foundational work proposed by John Daugman, but hitherto not used in generative AI experiments. The main objective of this study was to conduct a qualitative analysis of the synthetic samples and evaluate the iris texture density and suitability for meaningful feature extraction. During this study, a total of 1327 unique irises were generated, and experimental results carried out using the well-known OSIRIS open-source iris recognition software and the equivalent software, wordlcoin-openiris, newly published at the end of 2023 to prove that (1) no "identity leak" from the training set was observed, and (2) the generated irises had enough unique textural information to be successfully differentiated between both themselves and between them and real, authentic iris samples. The results of our research demonstrate the promising potential of synthetic iris data generation as a valuable tool for augmenting training datasets and improving the overall performance of iris recognition systems. By exploring the synthetic data in both Cartesian and polar representations, we aim to understand the benefits and limitations of each approach and their implications for biometric applications. The findings suggest that synthetic iris data can significantly contribute to the advancement of iris recognition technology, enhancing its accuracy and robustness in real-world scenarios by greatly augmenting the possibilities to gather large and diversified training datasets.
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Affiliation(s)
- Adrian Kordas
- Department of Biometrics, NASK—National Research Institute, 01-045 Warsaw, Poland (M.O.)
| | | | - Michał Ołowski
- Department of Biometrics, NASK—National Research Institute, 01-045 Warsaw, Poland (M.O.)
| | - Mateusz Trokielewicz
- Institute of Control and Computation Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland;
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Açιkyιldιz Ç. 'I know you like the back of my hand': biometric practices of humanitarian organisations in international aid. Disasters 2024; 48:e12612. [PMID: 37756185 DOI: 10.1111/disa.12612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/29/2023]
Abstract
Humanitarian organisations are increasingly utilising biometric data. However, we know little about the extent and scope of this practice, as its benefits and risks have attracted all the attention so far. This paper explores the biometric practices of the United Nations Refugee Agency, the United Nations World Food Programme, the International Committee of the Red Cross, Médecins Sans Frontières, and World Vision International. The study analysed relevant documents published over the past two decades and 17 semi-structured interviews with humanitarian workers conducted between June 2021 and June 2022. The findings reveal that humanitarian organisations use diverse types and functions of biometric data for different services, collaborate with many actors, and employ various data protection measures. Ultimately, challenging the straightforward generalisations about the use of such data, the paper argues that variational applications of biometrics in the humanitarian context require case-by-case analysis, as each instance will likely produce a different outcome.
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Affiliation(s)
- Çağlar Açιkyιldιz
- PhD Candidate, Pompeu Fabra University
- FI Predoctoral Fellow, Institut Barcelona d'Estudis Internacionals, Spain
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Vetter Hiebert JR, Petters Cabrera JG, Benítez del Puerto S, González Vatteone R, Florentín Morel M, Dacak Aguilera DA, Brítez Valinotti CE, Ramírez Diarte R, González González LM, Coronel Díaz C, Osorio P, Cardozo W, Bracho F, Soto CR, Domínguez Barreto NM, Sciabarrasi AA. Rescue and rehabilitation of maned wolf (Chrysocyon brachyurus) in Paraguay: Case description. Vet Med Sci 2024; 10:e1395. [PMID: 38459818 PMCID: PMC10924275 DOI: 10.1002/vms3.1395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 01/25/2024] [Accepted: 02/08/2024] [Indexed: 03/10/2024] Open
Abstract
The maned wolf, Chrysocyon brachyurus, is the largest South American canid, with a natural distribution that stretches across Peru, Bolivia, Brazil, Argentina, Paraguay and Uruguay. The present study reports the case of a rescued specimen of maned wolf that underwent a rehabilitation process in Paraguay, starting in October 2020 with its rescue, and finalising in May 2021 with the reintroduction. Herein, we document findings regarding the general management, biometrics, feeding and environmental enrichment; chemical immobilisation and monitoring; haematology, blood biochemistry and specific serology-relevant pathogens; skin examination and bone marrow cytology; orthopaedic, ophthalmological and dental evaluation; abdominal and cardiac ultrasonography; radiology and copro-parasitology. Main findings include the feeding habits of the individual and enrichment opportunities. The animal weighed 7 kg on arrival, with an estimated age of 5 months, and 18 kg on reintroduction, with an estimated age of 1 year. The animal tested negative to serologic tests for Brucella canis, Dirofilaria, canine distemper, Toxoplasmosis and canine parvovirus. Leptospira testing showed antibodies against L. grippotyphosa on both samplings, L. wolffi and L. ictero on the first sampling, and L. pomona on the second sampling. Abdominal organs were examined and measured through ultrasound evaluation and kidneys showed no alterations. Echocardiography showed preserved mitral, tricuspid and aortic valve flows, but turbulent pulmonary valve flow. Copro-parasitology reported the presence of Lagochilascaris sp. and Balantidium sp. All the information gathered aided in diagnosing the health status of the individual, and the response to environmental enrichment helped assess the behaviour, which led to the suggestion of reintroducing the animal. These data constitute the first published health check of a maned wolf in Paraguay, which can contribute to the species' conservation in the country. The protocol presented in this study can serve as a basis for developing an action plan for the maned wolf in Paraguay.
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Affiliation(s)
- Joerg Richard Vetter Hiebert
- Departamento de Recursos Faunísticos y Medio NaturalFacultad de Ciencias VeterinariasUniversidad Nacional de AsunciónSan LorenzoParaguay
| | | | | | - Roger González Vatteone
- Facultad de Ciencias VeterinariasCátedra de Técnica OperatoriaUniversidad Nacional de AsunciónSan LorenzoParaguay
| | | | | | | | | | | | - Carlos Coronel Díaz
- Ministerio del Ambiente y Desarrollo SostenibleDirección General de Protección y Conservación de la BiodiversidadDirección de Vida SilvestreAsunciónParaguay
| | | | | | - Fátima Bracho
- Facultad de Ciencias VeterinariasUniversidad Nacional de AsunciónSan LorenzoParaguay
| | - Claudia Raquel Soto
- Facultad de Ciencias VeterinariasUniversidad Nacional de AsunciónSan LorenzoParaguay
| | | | - Antonio Alejandro Sciabarrasi
- Facultad de Ciencias VeterinariasUniversidad Nacional del LitoralSanta FeArgentina
- Centro de rescate e interpretación de la Fauna La EsmeraldaGobierno de Santa FeArgentina
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Hwang HB, Lee J, Kwon H, Chung B, Lee J, Kim IY. Preliminary Study of Novel Bio-Crypto Key Generation Using Clustering-Based Binarization of ECG Features. Sensors (Basel) 2024; 24:1556. [PMID: 38475091 DOI: 10.3390/s24051556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 02/21/2024] [Accepted: 02/27/2024] [Indexed: 03/14/2024]
Abstract
In modern society, the popularity of wearable devices has highlighted the need for data security. Bio-crypto keys (bio-keys), especially in the context of wearable devices, are gaining attention as a next-generation security method. Despite the theoretical advantages of bio-keys, implementing such systems poses practical challenges due to their need for flexibility and convenience. Electrocardiograms (ECGs) have emerged as a potential solution to these issues but face hurdles due to intra-individual variability. This study aims to evaluate the possibility of a stable, flexible, and convenient-to-use bio-key using ECGs. We propose an approach that minimizes biosignal variability using normalization, clustering-based binarization, and the fuzzy extractor, enabling the generation of personalized seeds and offering ease of use. The proposed method achieved a maximum entropy of 0.99 and an authentication accuracy of 95%. This study evaluated various parameter combinations for generating effective bio-keys for personal authentication and proposed the optimal combination. Our research holds potential for security technologies applicable to wearable devices and healthcare systems.
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Affiliation(s)
- Ho Bin Hwang
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Jeyeon Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - Hyeokchan Kwon
- Information Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
| | - Byungho Chung
- Information Security Research Division, Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Republic of Korea
| | - Jongshill Lee
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
| | - In Young Kim
- Department of Biomedical Engineering, Hanyang University, Seoul 04763, Republic of Korea
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Martins N, Silva JS, Bernardino A. Fingerprint Recognition in Forensic Scenarios. Sensors (Basel) 2024; 24:664. [PMID: 38276355 PMCID: PMC10819264 DOI: 10.3390/s24020664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/15/2024] [Accepted: 01/17/2024] [Indexed: 01/27/2024]
Abstract
Fingerprints are unique patterns used as biometric keys because they allow an individual to be unambiguously identified, making their application in the forensic field a common practice. The design of a system that can match the details of different images is still an open problem, especially when applied to large databases or, to real-time applications in forensic scenarios using mobile devices. Fingerprints collected at a crime scene are often manually processed to find those that are relevant to solving the crime. This work proposes an efficient methodology that can be applied in real time to reduce the manual work in crime scene investigations that consumes time and human resources. The proposed methodology includes four steps: (i) image pre-processing using oriented Gabor filters; (ii) the extraction of minutiae using a variant of the Crossing Numbers method which include a novel ROI definition through convex hull and erosion followed by replacing two or more very close minutiae with an average minutiae; (iii) the creation of a model that represents each minutia through the characteristics of a set of polygons including neighboring minutiae; (iv) the individual search of a match for each minutia in different images using metrics on the absolute and relative errors. While in the literature most methodologies look to validate the entire fingerprint model, connecting the minutiae or using minutiae triplets, we validate each minutia individually using n-vertex polygons whose vertices are neighbor minutiae that surround the reference. Our method also reveals robustness against false minutiae since several polygons are used to represent the same minutia, there is a possibility that even if there are false minutia, the true polygon is present and identified; in addition, our method is immune to rotations and translations. The results show that the proposed methodology can be applied in real time in standard hardware implementation, with images of arbitrary orientations.
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Affiliation(s)
- Nuno Martins
- Portuguese Military Academy, 1169-203 Lisbon, Portugal;
- Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal;
| | - José Silvestre Silva
- Portuguese Military Academy, 1169-203 Lisbon, Portugal;
- Military Academy Research Center (CINAMIL), 1169-203 Lisbon, Portugal
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics, Universidade de Coimbra (LIBPhys-UC), 3000-370 Coimbra, Portugal
| | - Alexandre Bernardino
- Instituto Superior Técnico, Universidade de Lisboa, 1049-001 Lisbon, Portugal;
- Institute for Systems and Robotics (ISR), 1049-001 Lisbon, Portugal
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Brener S, Snitz K, Sobel N. An electronic nose can identify humans by the smell of their ear. Chem Senses 2024; 49:bjad053. [PMID: 38237638 PMCID: PMC10810274 DOI: 10.1093/chemse/bjad053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Indexed: 01/27/2024] Open
Abstract
Terrestrial mammals identify conspecifics by body odor. Dogs can also identify humans by body odor, and in some instances, humans can identify other humans by body odor as well. Despite the potential for a powerful biometric tool, smell has not been systematically used for this purpose. A question arising in the application of smell to biometrics is which bodily odor source should we measure. Breath is an obvious candidate, but the associated humidity can challenge many sensing devices. The armpit is also a candidate source, but it is often doused in cosmetics. Here, we test the hypothesis that the ear may provide an effective source for odor-based biometrics. The inside of the ear has relatively constant humidity, cosmetics are not typically applied inside the ear, and critically, ears contain cerumen, a potent source of volatiles. We used an electronic nose to identify 12 individuals within and across days, using samples from the armpit, lower back, and ear. In an identification setting where chance was 8.33% (1 of 12), we found that we could identify a person by the smell of their ear within a day at up to ~87% accuracy (~10 of 12, binomial P < 10-5), and across days at up to ~22% accuracy (~3 of 12, binomial P < 0.012). We conclude that humans can indeed be identified from the smell of their ear, but the results did not imply a consistent advantage over other bodily odor sources.
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Affiliation(s)
- Stephanie Brener
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Kobi Snitz
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
| | - Noam Sobel
- The Azrieli National Center for Human Brain Imaging and Research, Weizmann Institute of Science, Rehovot 7610001, Israel
- The Department for Brain Sciences, Weizmann Institute of Science, Rehovot 7610001, Israel
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Weeks M, Delgado AD, Wood J, Zhang B, Pesce S, Kunces L, Lili L, Putrino D. Relationships between body composition, anthropometrics, and standard lipid panels in a normative population. Front Cardiovasc Med 2023; 10:1280179. [PMID: 38124898 PMCID: PMC10731366 DOI: 10.3389/fcvm.2023.1280179] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 11/20/2023] [Indexed: 12/23/2023] Open
Abstract
Introduction More than one third of adults in the United States (US) meet the clinical criteria for a diagnosis of metabolic syndrome, but often diagnosis is challenging due to healthcare access, costs and discomfort with the process and invasiveness associated with a standard medical examination. Less invasive and more accessible approaches to collecting biometric data may have utility in identifying individuals at risk of diagnoses, such as metabolic syndrome or dyslipidemia diagnoses. Body composition is one such source of biometric data that can be non-invasively acquired in a home or community setting that may provide insight into an individual's propensity for a metabolic syndrome diagnosis. Here we investigate possible associations between body composition, anthropometrics and lipid panels in a normative population. Methods Healthy participants visited the Lab100 clinic location at a hospital setting in New York City and engaged in a wellness visit led by a nurse practitioner. Blood was analyzed at point-of-care using the Abbott Piccolo Xpress portable diagnostic analyzer (Abbott Laboratories, IL, USA) and produced direct measures of total cholesterol (TC), high density lipoprotein (HDL-C), low density lipoprotein (LDL-C), very-low density lipoprotein (VLDL-C), and triglycerides (TG). Body composition and anthropometric data were collected using two separate pieces of equipment during the same visit (Fit3D and InBody570). Regression analysis was performed to evaluate associations between all variables, after adjusting for age, sex, race, AUDIT-C total score (alcohol use), and current smoking status. Results Data from 199 participants were included in the analysis. After adjusting for variables, percentage body fat (%BF) and visceral fat levels were significantly associated with every laboratory lipid value, while waist-to-hip ratio also showed some significant associations. The strongest associations were detected between %BF and VLDL-C cholesterol levels (t = 4.53, p = 0.0001) and Triglyceride levels (t = 4.51, p = 0.0001). Discussion This initial, exploratory analysis shows early feasibility in using body composition and anthropometric data, that can easily be acquired in community settings, to identify people with dyslipidemia in a normative population.
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Affiliation(s)
- Marcus Weeks
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Andrew D. Delgado
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Jamie Wood
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Bodi Zhang
- Thorne HealthTech Inc., New York, NY, United States
| | - Sarah Pesce
- Thorne HealthTech Inc., New York, NY, United States
| | - Laura Kunces
- Thorne HealthTech Inc., New York, NY, United States
| | - Loukia Lili
- Thorne HealthTech Inc., New York, NY, United States
| | - David Putrino
- Department of Rehabilitation and Human Performance, Icahn School of Medicine at Mount Sinai, New York, NY, United States
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Roberts CH, Stott C, Shawe-Taylor M, Chaudhry Z, Lal S, Marks M. Biometric linkage of longitudinally collected electronic case report forms and confirmation of subject identity: an open framework for ODK and related tools. Front Digit Health 2023; 5:1072331. [PMID: 37600479 PMCID: PMC10436742 DOI: 10.3389/fdgth.2023.1072331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 07/25/2023] [Indexed: 08/22/2023] Open
Abstract
The availability of low-cost biometric hardware sensors and software makes it possible to rapidly, affordably and securely sample and store a unique and invariant biological signature (or biometric "template") for the purposes of identification. This has applications in research and trials, particularly for purposes of consent, linkage of case reporting forms collected at different times, and in the confirmation of participant identity for purposes of safety monitoring and adherence to international data laws. More broadly, these methods are applicable to the needs of the billion people who live in resource-restricted settings without identification credentials. The use of mobile electronic data collection software has recently become commonplace in clinical trials, research and actions for public good. A raft of tools based on the open-source ODK project now provide diverse options for data management that work consistently in resource-restricted settings, but none have built-in functionality for capturing biometric templates. In this study, we report the development and validation of a novel open-source app and associated method for capturing and matching biometric fingerprint templates during data collection with the popular data platforms ODK, KoBoToolbox, SurveyCTO, Ona and CommCare. Using data from more than 1,000 fingers, we show that fingerprint templates can be used to link data records with high accuracy. The accuracy of this process increases through the linkage of multiple fingerprints to each data record. By focussing on publishing open-source code and documentation, and by using an affordable (<£50) and mass-produced model of fingerprint sensor, we are able to make this platform freely available to the large global user community that utilises ODK and related data collection systems.
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Affiliation(s)
- Chrissy h. Roberts
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | | | - Marianne Shawe-Taylor
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Zain Chaudhry
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
| | - Sham Lal
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Michael Marks
- Faculty of Infectious and Tropical Diseases, London School of Hygiene and Tropical Medicine, London, United Kingdom
- Hospital for Tropical Diseases, University College London Hospitals NHS Trust, London, United Kingdom
- Division of Infection and Immunity, University College London, London, United Kingdom
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11
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Hsia CH, Ke LY, Chen ST. Improved Lightweight Convolutional Neural Network for Finger Vein Recognition System. Bioengineering (Basel) 2023; 10:919. [PMID: 37627804 PMCID: PMC10451947 DOI: 10.3390/bioengineering10080919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/07/2023] [Accepted: 08/01/2023] [Indexed: 08/27/2023] Open
Abstract
Computer vision (CV) technology and convolutional neural networks (CNNs) demonstrate superior feature extraction capabilities in the field of bioengineering. However, during the capturing process of finger-vein images, translation can cause a decline in the accuracy rate of the model, making it challenging to apply CNNs to real-time and highly accurate finger-vein recognition in various real-world environments. Moreover, despite CNNs' high accuracy, CNNs require many parameters, and existing research has confirmed their lack of shift-invariant features. Based on these considerations, this study introduces an improved lightweight convolutional neural network (ILCNN) for finger vein recognition. The proposed model incorporates a diverse branch block (DBB), adaptive polyphase sampling (APS), and coordinate attention mechanism (CoAM) with the aim of improving the model's performance in accurately identifying finger vein features. To evaluate the effectiveness of the model in finger vein recognition, we employed the finger-vein by university sains malaysia (FV-USM) and PLUSVein dorsal-palmar finger-vein (PLUSVein-FV3) public database for analysis and comparative evaluation with recent research methodologies. The experimental results indicate that the finger vein recognition model proposed in this study achieves an impressive recognition accuracy rate of 99.82% and 95.90% on the FV-USM and PLUSVein-FV3 public databases, respectively, while utilizing just 1.23 million parameters. Moreover, compared to the finger vein recognition approaches proposed in previous studies, the ILCNN introduced in this work demonstrated superior performance.
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Affiliation(s)
- Chih-Hsien Hsia
- Department of Computer Science and Information Engineering, National Ilan University, Yilan County 26047, Taiwan;
- Department of Business Administration, Chaoyang University of Technology, Taichung City 413310, Taiwan
| | - Liang-Ying Ke
- Department of Computer Science and Information Engineering, National Ilan University, Yilan County 26047, Taiwan;
| | - Sheng-Tao Chen
- Department of Avionics Engineering, Republic of China Air Force Academy, Kaohsiung City 82047, Taiwan
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12
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Li M, Qi Y, Pan G. Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation. Bioengineering (Basel) 2023; 10:912. [PMID: 37627797 PMCID: PMC10451328 DOI: 10.3390/bioengineering10080912] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/19/2023] [Accepted: 07/30/2023] [Indexed: 08/27/2023] Open
Abstract
Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.
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Affiliation(s)
- Ming Li
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Yu Qi
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- Affiliated Mental Health Center & Hangzhou Seventh Peoples Hospital, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Gang Pan
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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13
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Li M, Qi Y, Pan G. Optimal Feature Analysis for Identification Based on Intracranial Brain Signals with Machine Learning Algorithms. Bioengineering (Basel) 2023; 10:801. [PMID: 37508828 PMCID: PMC10376518 DOI: 10.3390/bioengineering10070801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 06/05/2023] [Accepted: 06/29/2023] [Indexed: 07/30/2023] Open
Abstract
Biometrics, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these traditional biometrics are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on an electroencephalogram (EEG), which typically demonstrates unstable performance due to the low signal-to-noise ratio (SNR). Thus, in this paper, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of a high-performance brain biometric. Significantly, this is the first study to investigate the features of intracortical brain signals for identification. Specifically, several features based on local field potential are computed for identification, and their performance is compared with different machine learning algorithms. The results show that frequency domain features and time-frequency domain features are excellent for intra-day and inter-day identification. Furthermore, the energy features perform best among all features with 98% intra-day and 93% inter-day identification accuracy, which demonstrates the great potential of intracraial brain signals to be biometrics. This paper may serve as a guidance for future intracranial brain researches and the development of more reliable and high-performance brain biometrics.
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Affiliation(s)
- Ming Li
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
| | - Yu Qi
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- Affiliated Mental Health Center & Hangzhou Seventh Peoples Hospital, MOE Frontier Science Center for Brain Science and Brain-Machine Integration, Zhejiang University School of Medicine, Hangzhou 310030, China
| | - Gang Pan
- State Key Lab of Brain-Machine Intelligence, Hangzhou 310018, China
- College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China
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14
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Khaliluzzaman M, Uddin A, Deb K, Hasan MJ. Person Recognition Based on Deep Gait: A Survey. Sensors (Basel) 2023; 23:4875. [PMID: 37430786 DOI: 10.3390/s23104875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/12/2023] [Accepted: 05/16/2023] [Indexed: 07/12/2023]
Abstract
Gait recognition, also known as walking pattern recognition, has expressed deep interest in the computer vision and biometrics community due to its potential to identify individuals from a distance. It has attracted increasing attention due to its potential applications and non-invasive nature. Since 2014, deep learning approaches have shown promising results in gait recognition by automatically extracting features. However, recognizing gait accurately is challenging due to the covariate factors, complexity and variability of environments, and human body representations. This paper provides a comprehensive overview of the advancements made in this field along with the challenges and limitations associated with deep learning methods. For that, it initially examines the various gait datasets used in the literature review and analyzes the performance of state-of-the-art techniques. After that, a taxonomy of deep learning methods is presented to characterize and organize the research landscape in this field. Furthermore, the taxonomy highlights the basic limitations of deep learning methods in the context of gait recognition. The paper is concluded by focusing on the present challenges and suggesting several research directions to improve the performance of gait recognition in the future.
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Affiliation(s)
- Md Khaliluzzaman
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
- Department of Computer Science and Engineering, International Islamic University Chittagong, Chattogram 4318, Bangladesh
| | - Ashraf Uddin
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Kaushik Deb
- Department of Computer Science and Engineering, Chittagong University of Engineering & Technology, Chattogram 4349, Bangladesh
| | - Md Junayed Hasan
- National Subsea Centre, Robert Gordon University, Aberdeen AB10 7AQ, UK
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15
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Rastogi A, Bashar MA, Sheikh NA. Relation of Primary Fingerprint Patterns With Gender and Blood Group: A Dermatoglyphic Study From a Tertiary Care Institute in Eastern India. Cureus 2023; 15:e38459. [PMID: 37273387 PMCID: PMC10238317 DOI: 10.7759/cureus.38459] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/28/2023] [Indexed: 06/06/2023] Open
Abstract
Background Identification of an individual plays a vital part in any medicolegal investigation. The fingerprint is one of the oldest and most reliable biometric methods and is taken as legitimate proof of identification of an individual. Positive relationships between the fingerprint pattern and blood group and the fingerprint pattern and gender were demonstrated in some of the previous studies but weren't consistent across them. Objectives (a) To study the distribution of fingerprint patterns among the study participants by gender and ABO and Rh blood groups and (b) to find an association between the fingerprint pattern and gender and blood group. Methods A cross-sectional observational study was carried out in the year 2021 on 800 healthcare students and workers of All India Institute of Medical Sciences, Patna, Bihar, Eastern India having different ABO and Rh blood groups. Healthy individuals i.e., those who were not suffering from any illness which can affect the fingerprints, aged 18 years or above were included and individuals having hand or finger deformities or missing fingers, having an allergy to the ink pad, and having blood group diseases were excluded. Rolled imprints of all the 10 digits of the participants were taken on a white A4 size Performa and were classified into loops, whorl, arches, and composite. The distribution of the fingerprint patterns was then compared by gender, ABO and Rh blood group. Chi-square/Fischer exact tests were applied to compare two groups and find the association. P-value<0.05 was taken as statistically significant. Results The majority (66.0%) of the participants in the study were males with a male: female ratio of 1.9:1. Most common blood group was blood group B (37.7%) followed by O (29.8%), A (23.0%), and AB (9.5%). Rh-positive cases constituted around 96% of all the studied cases with the rest being Rh-ve. The general distribution of the fingerprint pattern showed a high frequency of loops registering 55.9%; followed by whorls (34.9%), arches (6.0%), and composite (3.1%). The distribution of fingerprint patterns among the male and female gender was found to be similar with no significant difference (p=0.11). However, the distribution of the finger patterns across the ABO blood groups showed a statistically significant difference (p=0.0003) whereas it was non-significant across the Rh blood groups (p=0.08). Conclusion This study concludes that the distribution of the primary fingerprint patterns relates to the "ABO" blood group but not to gender and Rh blood group. An individual's fingerprints may be used to predict his/her blood group and vice versa.
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Affiliation(s)
- Ashok Rastogi
- Forensic Medicine, All India Institute of Medical Sciences, Patna, Patna, IND
| | - Md Abu Bashar
- Community Medicine and Family Medicine, All India Institute of Medical Sciences, Gorakhpur, Gorakhpur, IND
| | - Nishat Ahmed Sheikh
- Forensic Medicine and Toxicology, All India Institute of Medical Sciences, Deoghar, Deoghar, IND
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16
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Singla R, Ringstrom C, Hu R, Hu Z, Lessoway V, Reid J, Nguan C, Rohling R. Automatic measurement of kidney dimensions in two-dimensional ultrasonography is comparable to expert sonographers. J Med Imaging (Bellingham) 2023; 10:034003. [PMID: 37304526 PMCID: PMC10248852 DOI: 10.1117/1.jmi.10.3.034003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/11/2023] [Accepted: 05/19/2023] [Indexed: 06/13/2023] Open
Abstract
Purpose Length and width measurements of the kidneys aid in the detection and monitoring of structural abnormalities and organ disease. Manual measurement results in intra- and inter-rater variability, is complex and time-consuming, and is fraught with error. We propose an automated approach based on machine learning for quantifying kidney dimensions from two-dimensional (2D) ultrasound images in both native and transplanted kidneys. Approach An nnU-net machine learning model was trained on 514 images to segment the kidney capsule in standard longitudinal and transverse views. Two expert sonographers and three medical students manually measured the maximal kidney length and width in 132 ultrasound cines. The segmentation algorithm was then applied to the same cines, region fitting was performed, and the maximum kidney length and width were measured. Additionally, single kidney volume for 16 patients was estimated using either manual or automatic measurements. Results The experts resulted in length of 84.8±26.4 mm [95% CI: 80.0, 89.6] and a width of 51.8±10.5 mm [49.9, 53.7]. The algorithm resulted a length of 86.3±24.4 [81.5, 91.1] and a width of 47.1±12.8 [43.6, 50.6]. Experts, novices, and the algorithm did not statistically significant differ from one another (p>0.05). Bland-Altman analysis showed the algorithm produced a mean difference of 2.6 mm (SD = 1.2) from experts, compared to novices who had a mean difference of 3.7 mm (SD = 2.9 mm). For volumes, mean absolute difference was 47 mL (31%) consistent with ∼1 mm error in all three dimensions. Conclusions This pilot study demonstrates the feasibility of an automatic tool to measure in vivo kidney biometrics of length, width, and volume from standard 2D ultrasound views with comparable accuracy and reproducibility to expert sonographers. Such a tool may enhance workplace efficiency, assist novices, and aid in tracking disease progression.
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Affiliation(s)
- Rohit Singla
- University of British Columbia, School of Biomedical Engineering, Applied Science and Medicine, Vancouver, British Columbia, Canada
| | - Cailin Ringstrom
- University of British Columbia, Applied Science, Electrical, and Computer Engineering, Vancouver, British Columbia, Canada
| | - Ricky Hu
- Queen’s University, Medicine, Kingston, Ontario, Canada
| | - Zoe Hu
- Queen’s University, Medicine, Kingston, Ontario, Canada
| | - Victoria Lessoway
- University of British Columbia, Applied Science, Electrical, and Computer Engineering, Vancouver, British Columbia, Canada
| | - Janice Reid
- University of British Columbia, Applied Science, Electrical, and Computer Engineering, Vancouver, British Columbia, Canada
| | - Christopher Nguan
- University of British Columbia, Medicine, Urologic Sciences, Vancouver, British Columbia, Canada
| | - Robert Rohling
- University of British Columbia, School of Biomedical Engineering, Applied Science and Medicine, Vancouver, British Columbia, Canada
- University of British Columbia, Applied Science, Electrical, and Computer Engineering, Vancouver, British Columbia, Canada
- University of British Columbia, Medicine, Urologic Sciences, Vancouver, British Columbia, Canada
- University of British Columbia, Applied Science, Mechanical Engineering, Vancouver, British Columbia, Canada
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17
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Ortega-Rodríguez J, Gómez-González JF, Pereda E. Selection of the Minimum Number of EEG Sensors to Guarantee Biometric Identification of Individuals. Sensors (Basel) 2023; 23:s23094239. [PMID: 37177443 PMCID: PMC10181121 DOI: 10.3390/s23094239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 04/12/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
Biometric identification uses person recognition techniques based on the extraction of some of their physical or biological properties, which make it possible to characterize and differentiate one person from another and provide irreplaceable and critical information that is suitable for application in security systems. The extraction of information from the electrical biosignal of the human brain has received a great deal of attention in recent years. Analysis of EEG signals has been widely used over the last century in medicine and as a basis for brain-machine interfaces (BMIs). In addition, the application of EEG signals for biometric recognition has recently been demonstrated. In this context, EEG-based biometric systems are often considered in two different applications: identification (one-to-many classification) and authentication (one-to-one or true/false classification). In this article, we establish a methodology for selecting and reducing the minimum number of EEG sensors necessary to carry out effective biometric identification of individuals. Two methodologies were applied, one based on principal component analysis and the other on the Wilcoxon signed-rank test in order to reduce the number of electrodes. This allowed us to identify, according to the methodology used, the areas of the cerebral cortex that would allow selection of the minimum number of electrodes necessary for the identification of individuals. The methodologies were applied to two databases, one with 13 people with self-collected recordings using low-cost EEG equipment, EMOTIV EPOC+, and another publicly available database with recordings from 109 people provided by the PhysioNet BCI.
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Affiliation(s)
- Jordan Ortega-Rodríguez
- Department of Industrial Engineering, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain
- IACTEC Medical Technology Group, Instituto de Astrofísica de Canarias (IAC), 38320 San Cristóbal de La Laguna, Spain
| | | | - Ernesto Pereda
- Department of Industrial Engineering, University of La Laguna, 38200 San Cristóbal de La Laguna, Spain
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18
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Oblak T, Haraksim R, Beslay L, Peer P. Probabilistic Fingermark Quality Assessment with Quality Region Localisation. Sensors (Basel) 2023; 23:4006. [PMID: 37112346 PMCID: PMC10145466 DOI: 10.3390/s23084006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 04/09/2023] [Accepted: 04/12/2023] [Indexed: 06/19/2023]
Abstract
The assessment of fingermark (latent fingerprint) quality is an intrinsic part of a forensic investigation. The fingermark quality indicates the value and utility of the trace evidence recovered from the crime scene in the course of a forensic investigation; it determines how the evidence will be processed, and it correlates with the probability of finding a corresponding fingerprint in the reference dataset. The deposition of fingermarks on random surfaces occurs spontaneously in an uncontrolled fashion, which introduces imperfections to the resulting impression of the friction ridge pattern. In this work, we propose a new probabilistic framework for Automated Fingermark Quality Assessment (AFQA). We used modern deep learning techniques, which have the ability to extract patterns even from noisy data, and combined them with a methodology from the field of eXplainable AI (XAI) to make our models more transparent. Our solution first predicts a quality probability distribution, from which we then calculate the final quality value and, if needed, the uncertainty of the model. Additionally, we complemented the predicted quality value with a corresponding quality map. We used GradCAM to determine which regions of the fingermark had the largest effect on the overall quality prediction. We show that the resulting quality maps are highly correlated with the density of minutiae points in the input image. Our deep learning approach achieved high regression performance, while significantly improving the interpretability and transparency of the predictions.
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Affiliation(s)
- Tim Oblak
- Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
- Joint Research Centre, European Commission, 21027 Ispra, Italy
| | - Rudolf Haraksim
- Joint Research Centre, European Commission, 21027 Ispra, Italy
| | - Laurent Beslay
- Joint Research Centre, European Commission, 21027 Ispra, Italy
| | - Peter Peer
- Faculty of Computer and Information Science, University of Ljubljana, 1000 Ljubljana, Slovenia
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19
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Yang W, Wang S, Cui H, Tang Z, Li Y. A Review of Homomorphic Encryption for Privacy-Preserving Biometrics. Sensors (Basel) 2023; 23:3566. [PMID: 37050626 PMCID: PMC10098691 DOI: 10.3390/s23073566] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/20/2023] [Accepted: 03/27/2023] [Indexed: 06/19/2023]
Abstract
The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.
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Affiliation(s)
- Wencheng Yang
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Song Wang
- School of Computing, Engineering and Mathematical Sciences, La Trobe University, Bundoora, VIC 3086, Australia
| | - Hui Cui
- Faculty of IT, Claytyon Campus, Monash University, Clayton, VIC 3800, Australia
| | - Zhaohui Tang
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
| | - Yan Li
- School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba, QLD 4350, Australia
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20
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Marattukalam F, Abdulla W, Cole D, Gulati P. Deep Learning-Based Wrist Vascular Biometric Recognition. Sensors (Basel) 2023; 23:3132. [PMID: 36991845 PMCID: PMC10051641 DOI: 10.3390/s23063132] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/16/2023] [Revised: 03/08/2023] [Accepted: 03/13/2023] [Indexed: 06/19/2023]
Abstract
The need for contactless vascular biometric systems has significantly increased. In recent years, deep learning has proven to be efficient for vein segmentation and matching. Palm and finger vein biometrics are well researched; however, research on wrist vein biometrics is limited. Wrist vein biometrics is promising due to it not having finger or palm patterns on the skin surface making the image acquisition process easier. This paper presents a deep learning-based novel low-cost end-to-end contactless wrist vein biometric recognition system. FYO wrist vein dataset was used to train a novel U-Net CNN structure to extract and segment wrist vein patterns effectively. The extracted images were evaluated to have a Dice Coefficient of 0.723. A CNN and Siamese Neural Network were implemented to match wrist vein images obtaining the highest F1-score of 84.7%. The average matching time is less than 3 s on a Raspberry Pi. All the subsystems were integrated with the help of a designed GUI to form a functional end-to-end deep learning-based wrist biometric recognition system.
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21
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Borza DL, Yaghoubi E, Frintrop S, Proença H. Adaptive Spatial Transformation Networks for Periocular Recognition. Sensors (Basel) 2023; 23:2456. [PMID: 36904666 PMCID: PMC10007286 DOI: 10.3390/s23052456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 02/10/2023] [Accepted: 02/21/2023] [Indexed: 06/18/2023]
Abstract
Periocular recognition has emerged as a particularly valuable biometric identification method in challenging scenarios, such as partially occluded faces due to COVID-19 protective masks masks, in which face recognition might not be applicable. This work presents a periocular recognition framework based on deep learning, which automatically localises and analyses the most important areas in the periocular region. The main idea is to derive several parallel local branches from a neural network architecture, which in a semi-supervised manner learn the most discriminative areas in the feature map and solve the identification problem solely upon the corresponding cues. Here, each local branch learns a transformation matrix that allows for basic geometrical transformations (cropping and scaling), which is used to select a region of interest in the feature map, further analysed by a set of shared convolutional layers. Finally, the information extracted by the local branches and the main global branch are fused together for recognition. The experiments carried out on the challenging UBIRIS-v2 benchmark show that by integrating the proposed framework with various ResNet architectures, we consistently obtain an improvement in mAP of more than 4% over the "vanilla" architecture. In addition, extensive ablation studies were performed to better understand the behavior of the network and how the spatial transformation and the local branches influence the overall performance of the model. The proposed method can be easily adapted to other computer vision problems, which is also regarded as one of its strengths.
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Affiliation(s)
- Diana Laura Borza
- Informatics Department, Faculty of Mathematics and Informatics, Babes Bolyai University, 1st Mihail Kogalniceanu Street, 400084 Cluj-Napoca, Romania
| | - Ehsan Yaghoubi
- Department of Informatics, Hamburg University, 177 Mittelweg, 20148 Hamburg, Germany
| | - Simone Frintrop
- Department of Informatics, Hamburg University, 177 Mittelweg, 20148 Hamburg, Germany
| | - Hugo Proença
- IT: Instituto de Telecomunicações, University of Beira Interior, Marquês de Ávila e Bolama, 6201-001 Covilhã, Portugal
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22
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Marciniak T, Stankiewicz A, Zaradzki P. Neural Networks Application for Accurate Retina Vessel Segmentation from OCT Fundus Reconstruction. Sensors (Basel) 2023; 23:1870. [PMID: 36850467 PMCID: PMC9968084 DOI: 10.3390/s23041870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 01/31/2023] [Accepted: 02/04/2023] [Indexed: 06/18/2023]
Abstract
The use of neural networks for retinal vessel segmentation has gained significant attention in recent years. Most of the research related to the segmentation of retinal blood vessels is based on fundus images. In this study, we examine five neural network architectures to accurately segment vessels in fundus images reconstructed from 3D OCT scan data. OCT-based fundus reconstructions are of much lower quality compared to color fundus photographs due to noise and lower and disproportionate resolutions. The fundus image reconstruction process was performed based on the segmentation of the retinal layers in B-scans. Three reconstruction variants were proposed, which were then used in the process of detecting blood vessels using neural networks. We evaluated performance using a custom dataset of 24 3D OCT scans (with manual annotations performed by an ophthalmologist) using 6-fold cross-validation and demonstrated segmentation accuracy up to 98%. Our results indicate that the use of neural networks is a promising approach to segmenting the retinal vessel from a properly reconstructed fundus.
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23
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Pereira TMC, Conceição RC, Sencadas V, Sebastião R. Biometric Recognition: A Systematic Review on Electrocardiogram Data Acquisition Methods. Sensors (Basel) 2023; 23:1507. [PMID: 36772546 PMCID: PMC9921530 DOI: 10.3390/s23031507] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 01/24/2023] [Accepted: 01/27/2023] [Indexed: 06/17/2023]
Abstract
In the last decades, researchers have shown the potential of using Electrocardiogram (ECG) as a biometric trait due to its uniqueness and hidden nature. However, despite the great number of approaches found in the literature, no agreement exists on the most appropriate methodology. This paper presents a systematic review of data acquisition methods, aiming to understand the impact of some variables from the data acquisition protocol of an ECG signal in the biometric identification process. We searched for papers on the subject using Scopus, defining several keywords and restrictions, and found a total of 121 papers. Data acquisition hardware and methods vary widely throughout the literature. We reviewed the intrusiveness of acquisitions, the number of leads used, and the duration of acquisitions. Moreover, by analyzing the literature, we can conclude that the preferable solutions include: (1) the use of off-the-person acquisitions as they bring ECG biometrics closer to viable, unconstrained applications; (2) the use of a one-lead setup; and (3) short-term acquisitions as they required fewer numbers of contact points, making the data acquisition of benefit to user acceptance and allow faster acquisitions, resulting in a user-friendly biometric system. Thus, this paper reviews data acquisition methods, summarizes multiple perspectives, and highlights existing challenges and problems. In contrast, most reviews on ECG-based biometrics focus on feature extraction and classification methods.
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Affiliation(s)
| | - Raquel C. Conceição
- Instituto de Biofísica e Engenharia Biomédica, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Vitor Sencadas
- Instituto de Materiais (CICECO), Departamento de Materiais e Cerâmica, Universidade de Aveiro, 3810-193 Aveiro, Portugal
| | - Raquel Sebastião
- IEETA, DETI, LASI, Universidade de Aveiro, 3810-193 Aveiro, Portugal
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24
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Alsafyani M, Alhomayani F, Alsuwat H, Alsuwat E. Face Image Encryption Based on Feature with Optimization Using Secure Crypto General Adversarial Neural Network and Optical Chaotic Map. Sensors (Basel) 2023; 23:1415. [PMID: 36772454 PMCID: PMC9921757 DOI: 10.3390/s23031415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/19/2023] [Accepted: 01/20/2023] [Indexed: 06/18/2023]
Abstract
Demand for data security is increasing as information technology advances. Encryption technology based on biometrics has advanced significantly to meet more convenient and secure needs. Because of the stability of face traits and the difficulty of counterfeiting, the iris method has become an essential research object in data security research. This study proposes a revolutionary face feature encryption technique that combines picture optimization with cryptography and deep learning (DL) architectures. To improve the security of the key, an optical chaotic map is employed to manage the initial standards of the 5D conservative chaotic method. A safe Crypto General Adversarial neural network and chaotic optical map are provided to finish the course of encrypting and decrypting facial images. The target field is used as a "hidden factor" in the machine learning (ML) method in the encryption method. An encrypted image is recovered to a unique image using a modernization network to achieve picture decryption. A region-of-interest (ROI) network is provided to extract involved items from encrypted images to make data mining easier in a privacy-protected setting. This study's findings reveal that the recommended implementation provides significantly improved security without sacrificing image quality. Experimental results show that the proposed model outperforms the existing models in terms of PSNR of 92%, RMSE of 85%, SSIM of 68%, MAP of 52%, and encryption speed of 88%.
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Affiliation(s)
- Majed Alsafyani
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Fahad Alhomayani
- Department of Computer Engineering, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
| | - Hatim Alsuwat
- Department of Computer Science, College of Computer and Information Systems, Umm Al-Qura University, Makkah 24382, Saudi Arabia
| | - Emad Alsuwat
- Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 26571, Saudi Arabia
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25
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Baek S, Kim J, Yu H, Yang G, Sohn I, Cho Y, Park C. Intelligent Feature Selection for ECG-Based Personal Authentication Using Deep Reinforcement Learning. Sensors (Basel) 2023; 23:1230. [PMID: 36772269 PMCID: PMC9920765 DOI: 10.3390/s23031230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/16/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
In this study, the optimal features of electrocardiogram (ECG) signals were investigated for the implementation of a personal authentication system using a reinforcement learning (RL) algorithm. ECG signals were recorded from 11 subjects for 6 days. Consecutive 5-day datasets (from the 1st to the 5th day) were trained, and the 6th dataset was tested. To search for the optimal features of ECG for the authentication problem, RL was utilized as an optimizer, and its internal model was designed based on deep learning structures. In addition, the deep learning architecture in RL was automatically constructed based on an optimization approach called Bayesian optimization hyperband. The experimental results demonstrate that the feature selection process is essential to improve the authentication performance with fewer features to implement an efficient system in terms of computation power and energy consumption for a wearable device intended to be used as an authentication system. Support vector machines in conjunction with the optimized RL algorithm yielded accuracy outcomes using fewer features that were approximately 5%, 3.6%, and 2.6% higher than those associated with information gain (IG), ReliefF, and pure reinforcement learning structures, respectively. Additionally, the optimized RL yielded mostly lower equal error rate (EER) values than the other feature selection algorithms, with fewer selected features.
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Affiliation(s)
- Suwhan Baek
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Juhyeong Kim
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Hyunsoo Yu
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Geunbo Yang
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
| | - Illsoo Sohn
- Department of Computer Science and Engineering, Seoul National University of Science and Technology, Seoul 01811, Republic of Korea
| | - Youngho Cho
- Department of Electrical and Communication Engineering, Daelim University, Kyoung 13916, Republic of Korea
| | - Cheolsoo Park
- Department of Computer Engineering, Kwangwoon University, Seoul 01897, Republic of Korea
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26
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Sandercock BK, Gratto‐Trevor CL. Breeding populations of Marbled Godwits and Willets have high annual survival and strong site fidelity to managed wetlands. Ecol Evol 2023; 13:e9667. [PMID: 36699575 PMCID: PMC9849706 DOI: 10.1002/ece3.9667] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 12/05/2022] [Accepted: 12/09/2022] [Indexed: 01/20/2023] Open
Abstract
The Prairie Pothole Region of central Canada supports a diverse community of breeding waterbirds, but many species have declining populations and the demographic mechanisms driving the declines remain unknown. We conducted a 7-year field study during 1995-2001 to investigate the demographic performance of Marbled Godwits (Limosa fedoa) and Willets (Tringa semipalmata) breeding in managed wetlands near Brooks, Alberta. Mark-recapture analyses based on Cormack-Jolly-Seber models revealed that the annual rates of apparent survival for Marbled Godwits ( ϕ ^ = 0.953 ± 0.012SE) and Willets ( ϕ ^ = 0.861 ± 0.015SE) are among the highest rates of survivorship reported for any breeding or nonbreeding population of large-bodied shorebirds. Our estimates of life expectancy for males were comparable to longevity records in godwits (17.3 years ±5.8SE vs. 25-29+ years) and willets (7.7 ± 1.5SE vs. 10+ years). The two species both showed strong breeding site fidelity but differed in rates of mate fidelity. Pairs that reunited and males that switched mates usually nested <300 m from their previous nests, whereas females that switched mates usually moved longer distances >1.1-1.5 km. Returning pairs usually reunited in godwits (85%) but not in willets (28%), possibly because of species differences in adult survival or patterns of migration. Baseline estimates of annual survival for banded-only birds will be useful for evaluating the potential effects of new tracking tags or the environmental changes that have occurred during the past 20 years. Conservation strategies for large-bodied shorebirds should be focused on reduction of exposure to anthropogenic mortality because low rates of natural mortality suggest that losses to collisions at breeding sites or harvest at nonbreeding areas are likely to cause additive mortality.
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Affiliation(s)
- Brett K. Sandercock
- Department of Terrestrial EcologyNorwegian Institute for Nature ResearchTrondheimNorway
| | - Cheri L. Gratto‐Trevor
- Science and Technology BranchEnvironment and Climate Change CanadaSaskatoonSaskatchewanCanada
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27
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Camara C, Peris-Lopez P, Safkhani M, Bagheri N. ECG Identification Based on the Gramian Angular Field and Tested with Individuals in Resting and Activity States. Sensors (Basel) 2023; 23:937. [PMID: 36679733 PMCID: PMC9862128 DOI: 10.3390/s23020937] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 01/07/2023] [Accepted: 01/10/2023] [Indexed: 06/17/2023]
Abstract
In the last decade, biosignals have attracted the attention of many researchers when designing novel biometrics systems. Many of these works use cardiac signals and their representation as electrocardiograms (ECGs). Nowadays, these solutions are even more realistic since we can acquire reliable ECG records by using wearable devices. This paper moves in that direction and proposes a novel approach for an ECG identification system. For that, we transform the ECG recordings into Gramian Angular Field (GAF) images, a time series encoding technique well-known in other domains but not very common with biosignals. Specifically, the time series is transformed using polar coordinates, and then, the cosine sum of the angles is computed for each pair of points. We present a proof-of-concept identification system built on a tuned VGG19 convolutional neural network using this approach. We confirm our proposal's feasibility through experimentation using two well-known public datasets: MIT-BIH Normal Sinus Rhythm Database (subjects at a resting state) and ECG-GUDB (individuals under four specific activities). In both scenarios, the identification system reaches an accuracy of 91%, and the False Acceptance Rate (FAR) is eight times higher than the False Rejection Rate (FRR).
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Affiliation(s)
- Carmen Camara
- Computer Science Department, Carlos III University of Madrid, 28911 Leganés, Spain
| | - Pedro Peris-Lopez
- Computer Science Department, Carlos III University of Madrid, 28911 Leganés, Spain
| | - Masoumeh Safkhani
- Computer Engineering Department, Shahid Rajaee Teacher Training University, Tehran 16788-15811, Iran
| | - Nasour Bagheri
- Electrical Engineering Department, Shahid Rajaee Teacher Training University, Tehran 16788-15811, Iran
- School of Computer Science (SCS), Institute for Research in Fundamental Sciences (IPM), Tehran 16788-15811, Iran
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28
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Akhtar Z. Deepfakes Generation and Detection: A Short Survey. J Imaging 2023; 9:jimaging9010018. [PMID: 36662116 PMCID: PMC9863015 DOI: 10.3390/jimaging9010018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/05/2023] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Advancements in deep learning techniques and the availability of free, large databases have made it possible, even for non-technical people, to either manipulate or generate realistic facial samples for both benign and malicious purposes. DeepFakes refer to face multimedia content, which has been digitally altered or synthetically created using deep neural networks. The paper first outlines the readily available face editing apps and the vulnerability (or performance degradation) of face recognition systems under various face manipulations. Next, this survey presents an overview of the techniques and works that have been carried out in recent years for deepfake and face manipulations. Especially, four kinds of deepfake or face manipulations are reviewed, i.e., identity swap, face reenactment, attribute manipulation, and entire face synthesis. For each category, deepfake or face manipulation generation methods as well as those manipulation detection methods are detailed. Despite significant progress based on traditional and advanced computer vision, artificial intelligence, and physics, there is still a huge arms race surging up between attackers/offenders/adversaries (i.e., DeepFake generation methods) and defenders (i.e., DeepFake detection methods). Thus, open challenges and potential research directions are also discussed. This paper is expected to aid the readers in comprehending deepfake generation and detection mechanisms, together with open issues and future directions.
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Affiliation(s)
- Zahid Akhtar
- Department of Network and Computer Security, State University of New York (SUNY) Polytechnic Institute, Utica, NY 13502, USA
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29
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Shioiri K, Saho K. Exploration of Effective Time-Velocity Distribution for Doppler-Radar-Based Personal Gait Identification Using Deep Learning. Sensors (Basel) 2023; 23:604. [PMID: 36679401 PMCID: PMC9864811 DOI: 10.3390/s23020604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 06/17/2023]
Abstract
Personal identification based on radar gait measurement is an important application of biometric technology because it enables remote and continuous identification of people, irrespective of the lighting conditions and subjects' outfits. This study explores an effective time-velocity distribution and its relevant parameters for Doppler-radar-based personal gait identification using deep learning. Most conventional studies on radar-based gait identification used a short-time Fourier transform (STFT), which is a general method to obtain time-velocity distribution for motion recognition using Doppler radar. However, the length of the window function that controls the time and velocity resolutions of the time-velocity image was empirically selected, and several other methods for calculating high-resolution time-velocity distributions were not considered. In this study, we compared four types of representative time-velocity distributions calculated from the Doppler-radar-received signals: STFT, wavelet transform, Wigner-Ville distribution, and smoothed pseudo-Wigner-Ville distribution. In addition, the identification accuracies of various parameter settings were also investigated. We observed that the optimally tuned STFT outperformed other high-resolution distributions, and a short length of the window function in the STFT process led to a reasonable accuracy; the best identification accuracy was 99% for the identification of twenty-five test subjects. These results indicate that STFT is the optimal time-velocity distribution for gait-based personal identification using the Doppler radar, although the time and velocity resolutions of the other methods were better than those of the STFT.
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30
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Derlatka M, Borowska M. Ensemble of Heterogeneous Base Classifiers for Human Gait Recognition. Sensors (Basel) 2023; 23:508. [PMID: 36617105 PMCID: PMC9824449 DOI: 10.3390/s23010508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/23/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Human gait recognition is one of the most interesting issues within the subject of behavioral biometrics. The most significant problems connected with the practical application of biometric systems include their accuracy as well as the speed at which they operate, understood both as the time needed to recognize a particular person as well as the time necessary to create and train a biometric system. The present study made use of an ensemble of heterogeneous base classifiers to address these issues. A Heterogeneous ensemble is a group of classification models trained using various algorithms and combined to output an effective recognition A group of parameters identified on the basis of ground reaction forces was accepted as input signals. The proposed solution was tested on a sample of 322 people (5980 gait cycles). Results concerning the accuracy of recognition (meaning the Correct Classification Rate quality at 99.65%), as well as operation time (meaning the time of model construction at <12.5 min and the time needed to recognize a person at <0.1 s), should be considered as very good and exceed in quality other methods so far described in the literature.
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31
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Derlatka M. Automatic gender and unilateral load state recognition for biometric purposes. Technol Health Care 2023; 31:2467-2475. [PMID: 37955071 DOI: 10.3233/thc-235012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2023]
Abstract
BACKGROUND Automatic recognition of a person's gender as well as his or her unilateral load state are issues that are often analyzed and utilized by a wide range of applications. For years, scientists have recognized human gait patterns for purposes connected to medical diagnoses, rehabilitation, sport, or biometrics. OBJECTIVE The present paper makes use of ground reaction forces (GRF) generated during human gait to recognize gender or the unilateral load state of a walking person as well as the combination of both of those characteristics. METHODS To solve the above-stated problem parameters calculated on the basis of all GRF components such as mean, variance, standard deviation of data, peak-to-peak amplitude, skewness, kurtosis, and Hurst exponent as well as leading classification algorithms including kNN, artificial neural networks, decision trees, and random forests, were utilized. Data were collected by means of Kistler's force plates during a study carried out at the Bialystok University of Technology on a sample of 214 people with a total of 7,316 recorded gait cycles. RESULTS The best results were obtained with the use of the kNN classifier which recognized the gender of the participant with an accuracy of 99.37%, the unilateral load state with an accuracy reaching 95.74%, and the combination of those two states with an accuracy of 95.31% which, when compared to results achieved by other authors are some of the most accurate. CONCLUSION The study has shown that the given set of parameters in combination with the kNN classifying algorithm allows for an effective automatic recognition of a person's gender as well as the presence of an asymmetrical load in the form of a hand-carried briefcase. The presented method can be used as a first stage in biometrics systems.
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Khondakar KR, Kaushik A. Role of Wearable Sensing Technology to Manage Long COVID. Biosensors (Basel) 2022; 13:62. [PMID: 36671900 PMCID: PMC9855989 DOI: 10.3390/bios13010062] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/19/2022] [Accepted: 12/26/2022] [Indexed: 06/17/2023]
Abstract
Long COVID consequences have changed the perception towards disease management, and it is moving towards personal healthcare monitoring. In this regard, wearable devices have revolutionized the personal healthcare sector to track and monitor physiological parameters of the human body continuously. This would be largely beneficial for early detection (asymptomatic and pre-symptomatic cases of COVID-19), live patient conditions, and long COVID monitoring (COVID recovered patients and healthy individuals) for better COVID-19 management. There are multitude of wearable devices that can observe various human body parameters for remotely monitoring patients and self-monitoring mode for individuals. Smart watches, smart tattoos, rings, smart facemasks, nano-patches, etc., have emerged as the monitoring devices for key physiological parameters, such as body temperature, respiration rate, heart rate, oxygen level, etc. This review includes long COVID challenges for frequent monitoring of biometrics and its possible solution with wearable device technologies for diagnosis and post-therapy of diseases.
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Affiliation(s)
- Kamil Reza Khondakar
- School of Health Sciences and Technology, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India
| | - Ajeet Kaushik
- NanoBioTech Laboratory, Department of Environmental Engineering, Florida Polytechnic University, Lakeland, FL 33805-8531, USA
- Department of Chemical Engineering, University of Johannesburg, Johannesburg 2094, South Africa
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33
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Alexiev K, Vakarelski T. Can Microsaccades Be Used for Biometrics? Sensors (Basel) 2022; 23:89. [PMID: 36616687 PMCID: PMC9824634 DOI: 10.3390/s23010089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 12/17/2022] [Accepted: 12/19/2022] [Indexed: 06/17/2023]
Abstract
Human eyes are in constant motion. Even when we fix our gaze on a certain point, our eyes continue to move. When looking at a point, scientists have distinguished three different fixational eye movements (FEM)-microsaccades, drift and tremor. The main goal of this paper is to investigate one of these FEMs-microsaccades-as a source of information for biometric analysis. The paper argues why microsaccades are preferred for biometric analysis over the other two fixational eye movements. The process of microsaccades' extraction is described. Thirteen parameters are defined for microsaccade analysis, and their derivation is given. A gradient algorithm was used to solve the biometric problem. An assessment of the weights of the different pairs of parameters in solving the biometric task was made.
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34
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Zhu Y, Wang M, Yin X, Zhang J, Meijering E, Hu J. Deep Learning in Diverse Intelligent Sensor Based Systems. Sensors (Basel) 2022; 23:s23010062. [PMID: 36616657 PMCID: PMC9823653 DOI: 10.3390/s23010062] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 12/06/2022] [Accepted: 12/14/2022] [Indexed: 05/27/2023]
Abstract
Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development of deep learning methods and have fundamentally transformed the way the data are acquired, processed, analyzed, and interpreted. With the rapid development of deep learning technology and its ever-increasing range of successful applications across diverse sensor systems, there is an urgent need to provide a comprehensive investigation of deep learning in this domain from a holistic view. This survey paper aims to contribute to this by systematically investigating deep learning models/methods and their applications across diverse sensor systems. It also provides a comprehensive summary of deep learning implementation tips and links to tutorials, open-source codes, and pretrained models, which can serve as an excellent self-contained reference for deep learning practitioners and those seeking to innovate deep learning in this space. In addition, this paper provides insights into research topics in diverse sensor systems where deep learning has not yet been well-developed, and highlights challenges and future opportunities. This survey serves as a catalyst to accelerate the application and transformation of deep learning in diverse sensor systems.
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Affiliation(s)
- Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Jue Zhang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
| | - Erik Meijering
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia
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35
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Amrouni N, Benzaoui A, Bouaouina R, Khaldi Y, Adjabi I, Bouglimina O. Contactless Palmprint Recognition Using Binarized Statistical Image Features-Based Multiresolution Analysis. Sensors (Basel) 2022; 22:9814. [PMID: 36560183 PMCID: PMC9782967 DOI: 10.3390/s22249814] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 12/03/2022] [Accepted: 12/12/2022] [Indexed: 06/17/2023]
Abstract
In recent years, palmprint recognition has gained increased interest and has been a focus of significant research as a trustworthy personal identification method. The performance of any palmprint recognition system mainly depends on the effectiveness of the utilized feature extraction approach. In this paper, we propose a three-step approach to address the challenging problem of contactless palmprint recognition: (1) a pre-processing, based on median filtering and contrast limited adaptive histogram equalization (CLAHE), is used to remove potential noise and equalize the images' lighting; (2) a multiresolution analysis is applied to extract binarized statistical image features (BSIF) at several discrete wavelet transform (DWT) resolutions; (3) a classification stage is performed to categorize the extracted features into the corresponding class using a K-nearest neighbors (K-NN)-based classifier. The feature extraction strategy is the main contribution of this work; we used the multiresolution analysis to extract the pertinent information from several image resolutions as an alternative to the classical method based on multi-patch decomposition. The proposed approach was thoroughly assessed using two contactless palmprint databases: the Indian Institute of Technology-Delhi (IITD) and the Chinese Academy of Sciences Institute of Automatisation (CASIA). The results are impressive compared to the current state-of-the-art methods: the Rank-1 recognition rates are 98.77% and 98.10% for the IITD and CASIA databases, respectively.
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Affiliation(s)
- Nadia Amrouni
- LIST Laboratory, University of M’Hamed Bougara Boumerdes, Avenue of Independence, Boumerdes 35000, Algeria
| | - Amir Benzaoui
- Electrical Engineering Department, University of Skikda, BP 26, El Hadaiek, Skikda 21000, Algeria
| | - Rafik Bouaouina
- PIMIS Laboratory, Electronics and Telecommunications Department, Université du 8 Mai 1945 Guelma, Guelma 24000, Algeria
| | - Yacine Khaldi
- LIMPAF Laboratory, Department of Computer Science, University of Bouira, Bouira 10000, Algeria
| | - Insaf Adjabi
- LIMPAF Laboratory, Department of Computer Science, University of Bouira, Bouira 10000, Algeria
| | - Ouahiba Bouglimina
- Higher School of Computer Science and Technology (ESTIN), Bejaia 06300, Algeria
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Benomar M, Cao S, Vishwanath M, Vo K, Cao H. Investigation of EEG-Based Biometric Identification Using State-of-the-Art Neural Architectures on a Real-Time Raspberry Pi-Based System. Sensors (Basel) 2022; 22:9547. [PMID: 36502248 PMCID: PMC9735871 DOI: 10.3390/s22239547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 12/01/2022] [Accepted: 12/02/2022] [Indexed: 06/17/2023]
Abstract
Despite the growing interest in the use of electroencephalogram (EEG) signals as a potential biometric for subject identification and the recent advances in the use of deep learning (DL) models to study neurological signals, such as electrocardiogram (ECG), electroencephalogram (EEG), electroretinogram (ERG), and electromyogram (EMG), there has been a lack of exploration in the use of state-of-the-art DL models for EEG-based subject identification tasks owing to the high variability in EEG features across sessions for an individual subject. In this paper, we explore the use of state-of-the-art DL models such as ResNet, Inception, and EEGNet to realize EEG-based biometrics on the BED dataset, which contains EEG recordings from 21 individuals. We obtain promising results with an accuracy of 63.21%, 70.18%, and 86.74% for Resnet, Inception, and EEGNet, respectively, while the previous best effort reported accuracy of 83.51%. We also demonstrate the capabilities of these models to perform EEG biometric tasks in real-time by developing a portable, low-cost, real-time Raspberry Pi-based system that integrates all the necessary steps of subject identification from the acquisition of the EEG signals to the prediction of identity while other existing systems incorporate only parts of the whole system.
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Affiliation(s)
- Mohamed Benomar
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA
| | - Steven Cao
- Northwood High School, Irvine, CA 92620, USA
| | - Manoj Vishwanath
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Khuong Vo
- Department of Computer Science, University of California, Irvine, CA 92697, USA
| | - Hung Cao
- Department of Electrical Engineering and Computer Science, University of California, Irvine, CA 92697, USA
- Department of Computer Science, University of California, Irvine, CA 92697, USA
- Department of Biomedical Engineering, University of California, Irvine, CA 92697, USA
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Ketola EC, Barankovich M, Schuckers S, Ray-Dowling A, Hou D, Imtiaz MH. Channel Reduction for an EEG-Based Authentication System While Performing Motor Movements. Sensors (Basel) 2022; 22:9156. [PMID: 36501858 PMCID: PMC9740146 DOI: 10.3390/s22239156] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/10/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
Commercial use of biometric authentication is becoming increasingly popular, which has sparked the development of EEG-based authentication. To stimulate the brain and capture characteristic brain signals, these systems generally require the user to perform specific activities such as deeply concentrating on an image, mental activity, visual counting, etc. This study investigates whether effective authentication would be feasible for users tasked with a minimal daily activity such as lifting a tiny object. With this novel protocol, the minimum number of EEG electrodes (channels) with the highest performance (ranked) was identified to improve user comfort and acceptance over traditional 32-64 electrode-based EEG systems while also reducing the load of real-time data processing. For this proof of concept, a public dataset was employed, which contains 32 channels of EEG data from 12 participants performing a motor task without intent for authentication. The data was filtered into five frequency bands, and 12 different features were extracted to train a random forest-based machine learning model. All channels were ranked according to Gini Impurity. It was found that only 14 channels are required to perform authentication when EEG data is filtered into the Gamma sub-band within a 1% accuracy of using 32-channels. This analysis will allow (a) the design of a custom headset with 14 electrodes clustered over the frontal and occipital lobe of the brain, (b) a reduction in data collection difficulty while performing authentication, (c) minimizing dataset size to allow real-time authentication while maintaining reasonable performance, and (d) an API for use in ranking authentication performance in different headsets and tasks.
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38
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Hua C, Pan Y, Li J, Wang Z. Gait Recognition by Combining the Long-Short-Term Attention Network and Personal Physiological Features. Sensors (Basel) 2022; 22:8779. [PMID: 36433380 PMCID: PMC9697901 DOI: 10.3390/s22228779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 10/30/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
Although gait recognition has been greatly improved by efforts from many researchers in recent years, its performance is still unsatisfactory due to the lack of gait information under the real scenariowhere only one or two images may be used for recognition. In this paper, a new gait recognition framework is brought about which can combine the long-short-term attention modules on silhouette images over the whole sequence and the real human physiological information calculated by a monocular image. The contributions of this work include the following: (1) Fusing the global long-term attention (GLTA) and local short-term attention (LSTA) over the whole query sequence to improve the gait recognition accuracy, where both the short-term gait feature (from two or three frames) and long-term feature (from the whole sequence) are extracted; (2) presenting a method to calculate the real personal static and dynamic physiological features through a single monocular image; (3) by efficiently applying the human physiological information, a new physiological feature extraction (PFE) network is proposed to concatenate the physiological information with silhouette for gait recognition. Through the experiments between the CASIA-B and Multi-state Gait datasets, the effectiveness and efficiency of the proposed method are proven. Under three different walking conditions of the CASIA-B dataset, the mean accuracy of rank-1 in our method is up to 89.6%, and in the Multi-state Gait dataset, wearing different clothes, the mean accuracy of rank-1 in our method is 2.4% higher than the other works.
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Affiliation(s)
- Chunsheng Hua
- Institute of Intelligent Robot and Pattern Recognition, College of Information, Liaoning University, No. 66 Chongshan Middle Road, Huanggu District, Shenyang 110036, China
| | - Yingjie Pan
- College of Information, Liaoning University, Shenyang 110036, China
| | - Jia Li
- Department of Endocrinology and Metabolism, The Fourth Affiliated Hospital of China Medical University, Shenyang 110096, China
| | - Zhibo Wang
- Shenyang Contain Electronic Technology Co., Ltd., Shenyang 110167, China
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Kirfel A, Scheer T, Jung N, Busch C. Robust Identification and Segmentation of the Outer Skin Layers in Volumetric Fingerprint Data. Sensors (Basel) 2022; 22:8229. [PMID: 36365934 PMCID: PMC9658246 DOI: 10.3390/s22218229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 10/13/2022] [Accepted: 10/19/2022] [Indexed: 06/16/2023]
Abstract
Despite the long history of fingerprint biometrics and its use to authenticate individuals, there are still some unsolved challenges with fingerprint acquisition and presentation attack detection (PAD). Currently available commercial fingerprint capture devices struggle with non-ideal skin conditions, including soft skin in infants. They are also susceptible to presentation attacks, which limits their applicability in unsupervised scenarios such as border control. Optical coherence tomography (OCT) could be a promising solution to these problems. In this work, we propose a digital signal processing chain for segmenting two complementary fingerprints from the same OCT fingertip scan: One fingerprint is captured as usual from the epidermis ("outer fingerprint"), whereas the other is taken from inside the skin, at the junction between the epidermis and the underlying dermis ("inner fingerprint"). The resulting 3D fingerprints are then converted to a conventional 2D grayscale representation from which minutiae points can be extracted using existing methods. Our approach is device-independent and has been proven to work with two different time domain OCT scanners. Using efficient GPGPU computing, it took less than a second to process an entire gigabyte of OCT data. To validate the results, we captured OCT fingerprints of 130 individual fingers and compared them with conventional 2D fingerprints of the same fingers. We found that both the outer and inner OCT fingerprints were backward compatible with conventional 2D fingerprints, with the inner fingerprint generally being less damaged and, therefore, more reliable.
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Affiliation(s)
- Alexander Kirfel
- Institute of Safety and Security Research, Bonn-Rhine-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Tobias Scheer
- Institute of Safety and Security Research, Bonn-Rhine-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Norbert Jung
- Institute of Safety and Security Research, Bonn-Rhine-Sieg University of Applied Sciences, 53757 Sankt Augustin, Germany
| | - Christoph Busch
- Norwegian Biometrics Laboratory, Norwegian University of Science and Technology, 2815 Gjøvik, Norway
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Reis WP, Moses O, Oh J, Wilson A, Gaio J, Dos Santos H. The Association between Lifestyle Risk Factors and COVID-19 Hospitalization in a Healthcare Institution. Am J Lifestyle Med 2022:15598276221135541. [PMCID: PMC9596386 DOI: 10.1177/15598276221135541] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
COVID-19 is an acute infectious respiratory disease caused by SARS-CoV-2, a subtype of the coronavirus. In addition to normal levels of biometric measures, a healthy lifestyle has been considered an indispensable element in preventing complications of coronavirus infection. Demographic characteristics are also critical in determining risk levels. Aim: Investigate potential significant associations between health behaviors, biometric screenings, demographics, and COVID-19 hospitalization in Loma Linda University Health employees. Methods: Participants are employees covered under the employer-sponsored health plan at Loma Linda University Health, Loma Linda, CA, who tested positive for COVID-19. Logistic regression models were applied to analyze demographics, biometric screenings, and lifestyle factors associated with COVID-19 hospitalization. In our study, 7% of participants required hospitalization. Variables independently associated with COVID-19 hospitalization included higher age (OR = 1.05 [1.01–1.08], P = .005), non-White race compared to the White race (OR = 3.2 [1.22–8.38], P = .018), higher HbA1C levels showing a marginal association (OR = 1.31 [.99–1.72], P = .057), and lower vegetable consumption (OR = 4.39 [2.06–9.40], P < .001). Lower protein consumption decreased the Odds of hospitalization (OR = .40 [.19–.87], P = .021). Our results suggest that a diet that includes more vegetables and lower protein may confer some protection against COVID-19 hospitalization.
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Affiliation(s)
- Wenes Pereira Reis
- Hildemar Dos Santos, Public Health, Loma Linda University, 24951 Circle Dr, Loma Linda, CA 92354, USA; e-mail:
| | | | | | | | | | - Hildemar Dos Santos
- Hildemar Dos Santos, Public Health, Loma Linda University, 24951 Circle Dr, Loma Linda, CA 92354, USA; e-mail:
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Singleton EH, Fieldhouse JLP, van 't Hooft JJ, Scarioni M, van Engelen MPE, Sikkes SAM, de Boer C, Bocancea D, van den Berg E, Scheltens P, van der Flier WM, Papma JM, Pijnenburg YAL, Ossenkoppele R. Social cognition deficits and biometric signatures in the behavioural variant of Alzheimer's disease. Brain 2022; 146:2163-2174. [PMID: 36268579 PMCID: PMC10151185 DOI: 10.1093/brain/awac382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Revised: 09/23/2022] [Accepted: 09/27/2022] [Indexed: 11/14/2022] Open
Abstract
The behavioral variant of Alzheimer's disease is characterized by early predominant behavioral changes, mimicking the behavioral variant of frontotemporal dementia, which is characterized by social cognition deficits and altered biometric responses to socioemotional cues. These functions remain understudied in the behavioral variant of Alzheimer's disease. We investigated multiple social cognition components(i.e., emotion recognition, empathy, social norms and moral reasoning), using the Ekman-60-faces-test, Interpersonal-Reactivity-Index, empathy eliciting videos, Social-Norms-Questionnaire and moral dilemmas, while measuring eye-movements and galvanic skin response. We compared 12 patients with the behavioral variant of Alzheimer's disease with patients with the behavioral variant of frontotemporal dementia(n = 14), typical Alzheimer's disease(n = 13) and individuals with subjective cognitive decline(n = 13), using ANCOVAs and age and sex adjusted post hoc testing. Patients with the behavioral variant of Alzheimer's disease(40.1 ± 8.6) showed lower scores on the Ekman-60-faces-test compared to individuals with subjective cognitive decline(49.7 ± 5.0, p < 0.001), and patients with typical Alzheimer's disease(46.2 ± 5.3, p = 0.05) and higher scores compared to patients with behavioral variant of frontotemporal dementia(32.4 ± 7.3, p = 0.002). Eye-tracking during the Ekman-60-faces-test revealed no differences in dwell time on the eyes(all p > 0.05), but patients with the behavioral variant of Alzheimer's disease(18.7 ± 9.5%) and frontotemporal dementia(19.4 ± 14.3%) spent significantly less dwell time on the mouth than individuals with subjective cognitive decline(30.7 ± 11.6%, p < 0.01) and patients with typical Alzheimer's disease(32.7 ± 12.1%, p < 0.01). Patients with the behavioral variant of Alzheimer's disease(11.3 ± 4.6) exhibited lower scores on the Interpersonal-Reactivity-Index compared with individuals with subjective cognitive decline(15.6 ± 3.1, p = 0.05) and similar scores to patients with the behavioral variant of frontotemporal dementia(8.7 ± 5.6, p = 0.19) and typical Alzheimer's disease(13.0 ± 3.2, p = 0.43). The galvanic skin response to empathy eliciting videos did not differ between groups(all p > 0.05). Patients with the behavioral variant of Alzheimer's disease(16.0 ± 1.6) and frontotemporal dementia(15.2 ± 2.2) showed lower scores on the Social-Norms-Questionnaire than patients with typical Alzheimer's disease(17.8 ± 2.1, p < 0.05) and individuals with subjective cognitive decline(18.3 ± 1.4, p < 0.05). No group differences were observed in scores on moral dilemmas(all p > 0.05), while only patients with the behavioral variant of frontotemporal dementia(0.9 ± 1.1) showed a lower galvanic skin response during personal dilemmas compared with subjective cognitive decline(3.4 ± 3.3 peaks per minute, p = 0.01). Concluding, patients with the behavioral variant of Alzheimer's disease showed a similar though milder social cognition profile and a similar eye-tracking signature to patients with the behavioral variant of frontotemporal dementia and greater social cognition impairments and divergent eye-movement patterns compared with patients with typical Alzheimer's disease. Our results suggest reduced attention to salient facial features in these phenotypes, potentially contributing to their emotion recognition deficits.
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Affiliation(s)
- Ellen H Singleton
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jay L P Fieldhouse
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Jochum J van 't Hooft
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marta Scarioni
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Marie-Paule E van Engelen
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Sietske A M Sikkes
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Clinical, Neuro and Developmental Psychology, Faculty of Movement and Behavioural Sciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Casper de Boer
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Diana Bocancea
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Esther van den Berg
- Department of Epidemiology & Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Wiesje M van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Department of Epidemiology & Data Science, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Janne M Papma
- Department of Neurology and Alzheimer Center Erasmus MC, Erasmus MC University Medical Center, Rotterdam, The Netherlands
| | - Yolande A L Pijnenburg
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands.,Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Sweden
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Caba J, Barba J, Rincón F, de la Torre JA, Escolar S, López JC. Hyperspectral Face Recognition with Adaptive and Parallel SVMs in Partially Hidden Face Scenarios. Sensors (Basel) 2022; 22:7641. [PMID: 36236738 PMCID: PMC9570617 DOI: 10.3390/s22197641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Revised: 09/26/2022] [Accepted: 09/27/2022] [Indexed: 06/16/2023]
Abstract
Hyperspectral imaging opens up new opportunities for masked face recognition via discrimination of the spectral information obtained by hyperspectral sensors. In this work, we present a novel algorithm to extract facial spectral-features from different regions of interests by performing computer vision techniques over the hyperspectral images, particularly Histogram of Oriented Gradients. We have applied this algorithm over the UWA-HSFD dataset to extract the facial spectral-features and then a set of parallel Support Vector Machines with custom kernels, based on the cosine similarity and Euclidean distance, have been trained on fly to classify unknown subjects/faces according to the distance of the visible facial spectral-features, i.e., the regions that are not concealed by a face mask or scarf. The results draw up an optimal trade-off between recognition accuracy and compression ratio in accordance with the facial regions that are not occluded.
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Gupta MK, Viejo CG, Fuentes S, Torrico DD, Saturno PC, Gras SL, Dunshea FR, Cottrell JJ. Digital technologies to assess yoghurt quality traits and consumers acceptability. J Sci Food Agric 2022; 102:5642-5652. [PMID: 35368112 PMCID: PMC9544762 DOI: 10.1002/jsfa.11911] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Revised: 03/10/2022] [Accepted: 04/03/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND Sensory biometrics provide advantages for consumer tasting by quantifying physiological changes and the emotional response from participants, removing variability associated with self-reported responses. The present study aimed to measure consumers' emotional and physiological responses towards different commercial yoghurts, including dairy and plant-based yoghurts. The physiochemical properties of these products were also measured and linked with consumer responses. RESULTS Six samples (Control, Coconut, Soy, Berry, Cookies and Drinkable) were evaluated for overall liking by n = 62 consumers using a nine-point hedonic scale. Videos from participants were recorded using the Bio-Sensory application during tasting to assess emotions and heart rate. Physicochemical parameters Brix, pH, density, color (L, a and b), firmness and near-infrared (NIR) spectroscopy were also measured. Principal component analysis and a correlation matrix were used to assess relationships between the measured parameters. Heart rate was positively related to firmness, yaw head movement and overall liking, which were further associated with the Cookies sample. Two machine learning regression models were developed using (i) NIR absorbance values as inputs to predict the physicochemical parameters (Model 1) and (ii) the outputs from Model 1 as inputs to predict consumers overall liking (Model 2). Both models presented very high accuracy (Model 1: R = 0.98; Model 2: R = 0.99). CONCLUSION The presented methods were shown to be highly accurate and reliable with respect to their potential use by the industry to assess yoghurt quality traits and acceptability. © 2022 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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Affiliation(s)
- Mitali K Gupta
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Future Food Hallmark Research InitiativeThe University of MelbourneParkvilleVICAustralia
| | - Claudia Gonzalez Viejo
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Digital Agriculture, Food and Wine groupThe University of MelbourneParkvilleVICAustralia
| | - Sigfredo Fuentes
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Digital Agriculture, Food and Wine groupThe University of MelbourneParkvilleVICAustralia
| | - Damir D Torrico
- Department of Wine, Food and Molecular BiosciencesLincoln UniversityLincolnNew Zealand
| | - Patrizia Camille Saturno
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Philippine Carabao Center (PCC), National Headquarters and Gene Pool, Science City of MuñozPalayanPhilippines
| | - Sally L Gras
- Future Food Hallmark Research InitiativeThe University of MelbourneParkvilleVICAustralia
- Department of Chemical Engineering and The Bio21 Molecular Science and Biotechnology InstituteThe University of MelbourneParkvilleVICAustralia
| | - Frank R Dunshea
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Future Food Hallmark Research InitiativeThe University of MelbourneParkvilleVICAustralia
- Faculty of Biological SciencesThe University of LeedsLeedsUK
| | - Jeremy J Cottrell
- School of Agriculture and Food, Faculty of Veterinary and Agricultural SciencesThe University of MelbourneParkvilleVICAustralia
- Future Food Hallmark Research InitiativeThe University of MelbourneParkvilleVICAustralia
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Konings D, Alam F, Faulkner N, de Jong C. Identity and Gender Recognition Using a Capacitive Sensing Floor and Neural Networks. Sensors (Basel) 2022; 22:7206. [PMID: 36236306 PMCID: PMC9571660 DOI: 10.3390/s22197206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/01/2022] [Accepted: 09/20/2022] [Indexed: 06/16/2023]
Abstract
In recent publications, capacitive sensing floors have been shown to be able to localize individuals in an unobtrusive manner. This paper demonstrates that it might be possible to utilize the walking characteristics extracted from a capacitive floor to recognize subject and gender. Several neural network-based machine learning techniques are developed for recognizing the gender and identity of a target. These algorithms were trained and validated using a dataset constructed from the information captured from 23 subjects while walking, alone, on the sensing floor. A deep neural network comprising a Bi-directional Long Short-Term Memory (BLSTM) provided the most accurate identity performance, classifying individuals with an accuracy of 98.12% on the test data. On the other hand, a Convolutional Neural Network (CNN) was the most accurate for gender recognition, attaining an accuracy of 93.3%. The neural network-based algorithms are benchmarked against Support Vector Machine (SVM), which is a classifier used in many reported works for floor-based recognition tasks. The majority of the neural networks outperform SVM across all accuracy metrics.
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Lee J, Oh J, Kwon D, Kim M, Yu S, Jho NS, Park Y. PUFTAP-IoT: PUF-Based Three-Factor Authentication Protocol in IoT Environment Focused on Sensing Devices. Sensors (Basel) 2022; 22:7075. [PMID: 36146423 PMCID: PMC9506519 DOI: 10.3390/s22187075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Revised: 09/14/2022] [Accepted: 09/16/2022] [Indexed: 06/16/2023]
Abstract
In IoT-based environments, smart services can be provided to users under various environments, such as smart homes, smart factories, smart cities, smart transportation, and healthcare, by utilizing sensing devices. Nevertheless, a series of security problems may arise because of the nature of the wireless channel in the Wireless Sensor Network (WSN) for utilizing IoT services. Authentication and key agreements are essential elements for providing secure services in WSNs. Accordingly, two-factor and three-factor-based authentication protocol research is being actively conducted. However, IoT service users can be vulnerable to ID/password pair guessing attacks by setting easy-to-remember identities and passwords. In addition, sensors and sensing devices deployed in IoT environments are vulnerable to capture attacks. To address this issue, in this paper, we analyze the protocols of Chunka et al., Amintoosi et al., and Hajian et al. and describe their security vulnerabilities. Moreover, this paper introduces PUF and honey list techniques with three-factor authentication to design protocols resistant to ID/password pair guessing, brute-force, and capture attacks. Accordingly, we introduce PUFTAP-IoT, which can provide secure services in the IoT environment. To prove the security of PUFTAP-IoT, we perform formal analyses through Burrows Abadi Needham (BAN) logic, Real-Or-Random (ROR) model, and scyther simulation tools. In addition, we demonstrate the efficiency of the protocol compared with other authentication protocols in terms of security, computational cost, and communication cost, showing that it can provide secure services in IoT environments.
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Affiliation(s)
- JoonYoung Lee
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
| | - JiHyeon Oh
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
| | - DeokKyu Kwon
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
| | - MyeongHyun Kim
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
| | - SungJin Yu
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
- Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Nam-Su Jho
- Electronics and Telecommunications Research Institute, Daejeon 34129, Korea
| | - Youngho Park
- School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Korea
- School of Electronics Engineering, Kyungpook National University, Daegu 41566, Korea
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46
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Alotaibi A, Hussain M, AboAlSamh H, Abdul W, Bebis G. Cross-Sensor Fingerprint Enhancement Using Adversarial Learning and Edge Loss. Sensors (Basel) 2022; 22:6973. [PMID: 36146321 PMCID: PMC9504094 DOI: 10.3390/s22186973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/04/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
A fingerprint sensor interoperability problem, or a cross-sensor matching problem, occurs when one type of sensor is used for enrolment and a different type for matching. Fingerprints captured for the same person using various sensor technologies have various types of noises and artifacts. This problem motivated us to develop an algorithm that can enhance fingerprints captured using different types of sensors and touch technologies. Inspired by the success of deep learning in various computer vision tasks, we formulate this problem as an image-to-image transformation designed using a deep encoder-decoder model. It is trained using two learning frameworks, i.e., conventional learning and adversarial learning based on a conditional Generative Adversarial Network (cGAN) framework. Since different types of edges form the ridge patterns in fingerprints, we employed edge loss to train the model for effective fingerprint enhancement. The designed method was evaluated on fingerprints from two benchmark cross-sensor fingerprint datasets, i.e., MOLF and FingerPass. To assess the quality of enhanced fingerprints, we employed two standard metrics commonly used: NBIS Fingerprint Image Quality (NFIQ) and Structural Similarity Index Metric (SSIM). In addition, we proposed a metric named Fingerprint Quality Enhancement Index (FQEI) for comprehensive evaluation of fingerprint enhancement algorithms. Effective fingerprint quality enhancement results were achieved regardless of the sensor type used, where this issue was not investigated in the related literature before. The results indicate that the proposed method outperforms the state-of-the-art methods.
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Affiliation(s)
- Ashwaq Alotaibi
- Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia
| | - Muhammad Hussain
- Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia
| | - Hatim AboAlSamh
- Department of Computer Science, CCIS, King Saud University, Riyadh 11451, Saudi Arabia
| | - Wadood Abdul
- Department of Computer Engineering, King Saud University, Riyadh 11451, Saudi Arabia
| | - George Bebis
- Department of Computer Science and Engineering, University of Nevada, Reno, NV 89557, USA
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47
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Stergiadis C, Kostaridou VD, Veloudis S, Kazis D, Klados MA. A Personalized User Authentication System Based on EEG Signals. Sensors (Basel) 2022; 22:6929. [PMID: 36146276 PMCID: PMC9503240 DOI: 10.3390/s22186929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
Conventional biometrics have been employed in high-security user-authentication systems for over 20 years now. However, some of these modalities face low-security issues in common practice. Brainwave-based user authentication has emerged as a promising alternative method, as it overcomes some of these drawbacks and allows for continuous user authentication. In the present study, we address the problem of individual user variability, by proposing a data-driven Electroencephalography (EEG)-based authentication method. We introduce machine learning techniques, in order to reveal the optimal classification algorithm that best fits the data of each individual user, in a fast and efficient manner. A set of 15 power spectral features (delta, theta, lower alpha, higher alpha, and alpha) is extracted from three EEG channels. The results show that our approach can reliably grant or deny access to the user (mean accuracy of 95.6%), while at the same time poses a viable option for real-time applications, as the total time of the training procedure was kept under one minute.
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Affiliation(s)
- Christos Stergiadis
- Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece
- Neuroscience Research Center (NEUREC), City College, University of York Europe Campus, 54622 Thessaloniki, Greece
| | | | - Simos Veloudis
- Department of Computer Science, City College, University of York Europe Campus, 54622 Thessaloniki, Greece
| | - Dimitrios Kazis
- 3rd Department of Neurology, Aristotle University of Thessaloniki, Exochi, 57010 Thessaloniki, Greece
| | - Manousos A. Klados
- Department of Psychology, City College, University of York Europe Campus, 54622 Thessaloniki, Greece
- Neuroscience Research Center (NEUREC), City College, University of York Europe Campus, 54622 Thessaloniki, Greece
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48
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Wang X, Shi Y, Zheng K, Zhang Y, Hong W, Cao S. User Authentication Method Based on Keystroke Dynamics and Mouse Dynamics with Scene-Irrelated Features in Hybrid Scenes. Sensors (Basel) 2022; 22:6627. [PMID: 36081085 PMCID: PMC9460698 DOI: 10.3390/s22176627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/21/2022] [Accepted: 08/31/2022] [Indexed: 06/15/2023]
Abstract
In order to improve user authentication accuracy based on keystroke dynamics and mouse dynamics in hybrid scenes and to consider the user operation changes in different scenes that aggravate user status changes and make it difficult to simulate user behaviors, we present a user authentication method entitled SIURUA. SIURUA uses scene-irrelated features and user-related features for user identification. First, features are extracted based on keystroke data and mouse movement data. Next, scene-irrelated features that have a low correlation with scenes are obtained. Finally, scene-irrelated features are fused with user-related features to ensure the integrity of the features. Experimental results show that the proposed method has the advantage of improving user authentication accuracy in hybrid scenes, with an accuracy of 84% obtained in the experiment.
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Affiliation(s)
- Xiujuan Wang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Yutong Shi
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Kangfeng Zheng
- School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Yuyang Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Weijie Hong
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Siwei Cao
- Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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49
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Abstract
This article examines the sociotechnical imaginary within which contemporary biometric listening or VIA (voice identification and analysis) technologies are being developed. Starting from an examination of a key article on Voiceprint identification written in the 1940s, I interrogate the conceptual link between voice, body, and identity, which was central to these early attempts at technologizing voice identification. By surveying patents that delineate systems for voice identification, collection methods for voice data, and voice analysis, I find that the VIA industry is dependent on the conceptual affixion of voice to identity based on a reduction of voice that sees it as a fixed, extractable, and measurable 'sound object' located within the body. This informs the thinking of developers in the VIA industry, resulting in a reframing of the technological shortcomings of voice identification under the rubric of big data. Ultimately, this reframing rationalizes the implementation of audio surveillance systems into existing telecommunications infrastructures through which voice data is acquired on a massive scale.
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Affiliation(s)
- Edward B Kang
- University of Southern California, Los Angeles, CA, USA
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50
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Wang M, Yin X, Zhu Y, Hu J. Representation Learning and Pattern Recognition in Cognitive Biometrics: A Survey. Sensors (Basel) 2022; 22:5111. [PMID: 35890799 PMCID: PMC9320620 DOI: 10.3390/s22145111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Revised: 07/01/2022] [Accepted: 07/05/2022] [Indexed: 01/27/2023]
Abstract
Cognitive biometrics is an emerging branch of biometric technology. Recent research has demonstrated great potential for using cognitive biometrics in versatile applications, including biometric recognition and cognitive and emotional state recognition. There is a major need to summarize the latest developments in this field. Existing surveys have mainly focused on a small subset of cognitive biometric modalities, such as EEG and ECG. This article provides a comprehensive review of cognitive biometrics, covering all the major biosignal modalities and applications. A taxonomy is designed to structure the corresponding knowledge and guide the survey from signal acquisition and pre-processing to representation learning and pattern recognition. We provide a unified view of the methodological advances in these four aspects across various biosignals and applications, facilitating interdisciplinary research and knowledge transfer across fields. Furthermore, this article discusses open research directions in cognitive biometrics and proposes future prospects for developing reliable and secure cognitive biometric systems.
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Affiliation(s)
- Min Wang
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Xuefei Yin
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
| | - Yanming Zhu
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australia;
| | - Jiankun Hu
- School of Engineering and Information Technology, University of New South Wales, Canberra, ACT 2612, Australia; (M.W.); (X.Y.)
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