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Srivastava R, Bhardwaj VP, Othman MTB, Pushkarna M, Anushree, Mangla A, Bajaj M, Rehman AU, Shafiq M, Hamam H. Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier. SENSORS (BASEL, SWITZERLAND) 2022; 22:3620. [PMID: 35632035 PMCID: PMC9146366 DOI: 10.3390/s22103620] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Revised: 05/01/2022] [Accepted: 05/06/2022] [Indexed: 11/16/2022]
Abstract
Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger-knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.
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Affiliation(s)
- Rohit Srivastava
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India; (R.S.); (V.P.B.)
| | - Ved Prakash Bhardwaj
- School of Computer Science, University of Petroleum and Energy Studies, Dehradun 248007, India; (R.S.); (V.P.B.)
| | - Mohamed Tahar Ben Othman
- Department of Computer Science, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia
| | - Mukesh Pushkarna
- Department of Electrical Engineering, GLA University, Mathura 281406, India;
| | - Anushree
- Department of Computer Engineering and Applications, GLA University, Mathura 281406, India;
| | | | - Mohit Bajaj
- Department of Electrical and Electronics Engineering, National Institute of Technology, Delhi 110040, India;
| | - Ateeq Ur Rehman
- College of Internet of Things Engineering, Hohai University, Changzhou 213022, China;
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada;
| | - Muhammad Shafiq
- Department of Information and Communication Engineering, Yeungnam University, Gyeongsan 38541, Korea
| | - Habib Hamam
- Faculty of Engineering, Université de Moncton, Moncton, NB E1A3E9, Canada;
- International Institute of Technology and Management, Libreville BP1989, Gabon
- Spectrum of Knowledge Production and Skills Development, Sfax 3027, Tunisia
- Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg 2006, South Africa
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Mobile games success and failure: mining the hidden factors. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07154-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
AbstractPredicting the success of a mobile game is a prime issue in game industry. Thousands of games are being released each day. However, a few of them succeed while the majority fail. Toward the goal of investigating the potential correlation between the success of a mobile game and its specific attributes, this work was conducted. More than 17 thousand games were considered for that reason. We show that IAPs (In-App Purchases), genre, number of supported languages, developer profile, and release month have a clear effect on the success of a mobile game. We also develop a novel success score reflecting multiple objectives. Furthermore, we show that game icons with certain visual characteristics tend to be associated with more rating counts. We employ different machine learning models to predict a novel success score metric of a mobile game given its attributes. The trained models were able to predict this score, as well as the expected rating average and rating count for a mobile game with 70% accuracy.
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V SS, R RK. Iris Recognition using Multi Objective Artificial Bee Colony Optimization Algorithm with Autoencoder Classifier. Neural Process Lett 2022. [DOI: 10.1007/s11063-022-10775-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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Abstract
Biometric technology has received a lot of attention in recent years. One of the most prevalent biometric traits is the finger-knuckle print (FKP). Because the dorsal region of the finger is not exposed to surfaces, FKP would be a dependable and trustworthy biometric. We provide an FKP framework that uses the VGG-19 deep learning model to extract deep features from FKP images in this paper. The deep features are collected from the VGG-19 model’s fully connected layer 6 (F6) and fully connected layer 7 (F7). After applying multiple preprocessing steps, such as combining features from different layers and performing dimensionality reduction using principal component analysis (PCA), the extracted deep features are put to the test. The proposed system’s performance is assessed using experiments on the Delhi Finger Knuckle Dataset employing a variety of common classifiers. The best identification result was obtained when the Artificial neural network (ANN) classifier was applied to the principal components of the averaged feature vector of F6 and F7 deep features, with 95% of the data variance preserved. The findings also demonstrate the feasibility of employing these deep features in an FKP recognition system.
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Gayathri M, Malathy C. Novel framework for multimodal biometric image authentication using visual share neural network. Pattern Recognit Lett 2021. [DOI: 10.1016/j.patrec.2021.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Abdulhay E, Faust O. Preface of virtual special issue on Smart Pattern Recognition for Medical Informatics. Pattern Recognit Lett 2019. [DOI: 10.1016/j.patrec.2019.08.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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