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Ren LJ, Luo F, Yang ZW, Chen LL, Wang XY, Li CL, Xie YZ, Wang JM, Zhang TY, Wang S, Fu YY. A publicly available newborn ear shape dataset for medical diagnosis of auricular deformities. Sci Data 2024; 11:13. [PMID: 38167545 PMCID: PMC10762036 DOI: 10.1038/s41597-023-02834-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 12/07/2023] [Indexed: 01/05/2024] Open
Abstract
Early and accurate diagnosis of ear deformities in newborns is crucial for an effective non-surgical correction treatment, since this commonly seen ear anomalies would affect aesthetics and cause mental problems if untreated. It is not easy even for experienced physicians to diagnose the auricular deformities of newborns and the classification of the sub-types, because of the rich bio-metric features embedded in the ear shape. Machine learning has already been introduced to analyze the auricular shape. However, there is little publicly available datasets of ear images from newborns. We released a dataset that contains quality-controlled photos of 3,852 ears from 1,926 newborns. The dataset also contains medical diagnosis of the ear shape, and the health data of each newborn and its mother. Our aim is to provide a freely accessible dataset, which would facilitate researches related with ear anatomies, such as the AI-aided detection and classification of auricular deformities and medical risk analysis.
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Affiliation(s)
- Liu-Jie Ren
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Fei Luo
- Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Zhi-Wei Yang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Academy for Engineering & Technology, Fudan University, Shanghai, China
| | - Li-Li Chen
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China
| | - Xin-Yue Wang
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China
| | - Chen-Long Li
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - You-Zhou Xie
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China
| | - Ji-Mei Wang
- Obstetrics & Gynecology Hospital, Fudan University, Shanghai, China
| | - Tian-Yu Zhang
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.
| | - Shuo Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
- Academy for Engineering & Technology, Fudan University, Shanghai, China.
| | - Yao-Yao Fu
- FPRS Department/ENT Institute, Eye and ENT Hospital, Fudan University, Shanghai, China.
- NHC Key Laboratory of Hearing Medicine, Fudan University, Shanghai, China.
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A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last Decade. INFORMATION 2023. [DOI: 10.3390/info14030192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023] Open
Abstract
Biometric technology is fast gaining pace as a veritable developmental tool. So far, biometric procedures have been predominantly used to ensure identity and ear recognition techniques continue to provide very robust research prospects. This paper proposes to identify and review present techniques for ear biometrics using certain parameters: machine learning methods, and procedures and provide directions for future research. Ten databases were accessed, including ACM, Wiley, IEEE, Springer, Emerald, Elsevier, Sage, MIT, Taylor & Francis, and Science Direct, and 1121 publications were retrieved. In order to obtain relevant materials, some articles were excused using certain criteria such as abstract eligibility, duplicity, and uncertainty (indeterminate method). As a result, 73 papers were selected for in-depth assessment and significance. A quantitative analysis was carried out on the identified works using search strategies: source, technique, datasets, status, and architecture. A Quantitative Analysis (QA) of feature extraction methods was carried out on the selected studies with a geometric approach indicating the highest value at 36%, followed by the local method at 27%. Several architectures, such as Convolutional Neural Network, restricted Boltzmann machine, auto-encoder, deep belief network, and other unspecified architectures, showed 38%, 28%, 21%, 5%, and 4%, respectively. Essentially, this survey also provides the various status of existing methods used in classifying related studies. A taxonomy of the current methodologies of ear recognition system was presented along with a publicly available occlussion and pose sensitive black ear image dataset of 970 images. The study concludes with the need for researchers to consider improvements in the speed and security of available feature extraction algorithms.
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Aiadi O, Khaldi B, Saadeddine C. MDFNet: an unsupervised lightweight network for ear print recognition. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022; 14:1-14. [PMID: 35757492 PMCID: PMC9206135 DOI: 10.1007/s12652-022-04028-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
In this paper, we propose an unsupervised lightweight network with a single layer for ear print recognition. We refer to this method by MDFNet because it relies on gradient Magnitude and Direction alongside with responses of data-driven Filters. At first, we align ear using Convolution Neural Network (CNN) and Principal Component Analysis (PCA). MDFNet starts by generating a filter bank from training images using PCA. This is followed by a twofold layer, which comprises two operations namely convolution using learned filters and computation of gradient image. To prevent over-fitting, a binary hashing process is done by combining different filter responses into a single feature map. Then, we separately construct histograms for each of gradient magnitude and direction according to the feature map. These histograms are then normalized, using power-L2 rule, to cope with illumination disparity. Several fusion rules are evaluated to combine the two histograms. The main novelty of MDFNet lies in its simple architecture, effectiveness, the good compromise between processing time and performance it provides along with its high robustness to occlusion. We conduct extensive experiments on three public datasets namely AWE, AMI and IIT Delhi II. Experimental results demonstrate the effectiveness of MDFNet, which achieves high recognition rates (82.5%, 97.67% and 98.96%, respectively), and outperformed several state of the art methods with a high robustness to occlusion. Experiments revealed also the actual need for considering ear alignment.
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Affiliation(s)
- Oussama Aiadi
- LINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria
| | - Belal Khaldi
- LINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria
| | - Cheraa Saadeddine
- LINATI Laboratory, Department of Computer Science and Information Technology, University of Kasdi Merbah, 30000 Ouargla, Algeria
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Korichi A, Slatnia S, Aiadi O. TR-ICANet: A Fast Unsupervised Deep-Learning-Based Scheme for Unconstrained Ear Recognition. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2022. [DOI: 10.1007/s13369-021-06375-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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