research-article

Few-shot learning for ear recognition

Authors:

Fang DengAuthors Info & Claims

IVSP '19: Proceedings of the 2019 International Conference on Image, Video and Signal Processing

Pages 50 - 54

https://doi.org/10.1145/3317640.3317646

Published: 25 February 2019 Publication History

Abstract

Ear recognition is a popular field of research within the biometric community. It plays an important part in automatic recognition systems. The ability to capture image of the ear from a distance and perform identity recognition makes ear recognition technology an attractive choice for security application as well as other related applications. However, datasets of ear images are still limited in size, while in other biometric modal communities, like face recognition, they possess large datasets and the most of them are collected in uncontrolled condition. As a result, deep learning technology still cannot yield satisfactory result in ear recognition area. In this paper, we tackle ear recognition problem by using few-shot learning based methods. We explore different methods towards model training with limited amounts of training data and show that by using them, with the help of data augmentation, the model can be flexible and can quickly adapt to new identity to perform fast recognition. The result of our work is the first few-shot learning based work to ear recognition. With our work we are able to significantly improve the accuracy of 23% on a regular dataset, and even 21% on a challenging dataset that is collected from the web, which is comparable with state-of-the-art in ear recognition area.

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Cited By

Tuama SSaud JRashid O(2024)Automatic Identification of Ear Patterns Based on Convolutional Neural NetworkNew Trends in Information and Communications Technology Applications10.1007/978-3-031-62814-6_6(75-89)Online publication date: 26-Jun-2024
https://doi.org/10.1007/978-3-031-62814-6_6
Xu XLiu YLiu CLu L(2023)A Feature Fusion Human Ear Recognition Method Based on Channel Features and Dynamic ConvolutionSymmetry10.3390/sym1507145415:7(1454)Online publication date: 21-Jul-2023
https://doi.org/10.3390/sym15071454
Oyebiyi OAbayomi-Alli AArogundade OQazi AImoize AAwotunde J(2023)A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last DecadeInformation10.3390/info1403019214:3(192)Online publication date: 17-Mar-2023
https://doi.org/10.3390/info14030192
Show More Cited By

Index Terms

Few-shot learning for ear recognition
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
        Biometrics

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cover image ACM Other conferences

IVSP '19: Proceedings of the 2019 International Conference on Image, Video and Signal Processing

February 2019

140 pages

ISBN:9781450361750

DOI:10.1145/3317640

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Wuhan Univ.: Wuhan University, China
City University of Hong Kong: City University of Hong Kong

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 February 2019

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IVSP 2019

IVSP 2019: 2019 International Conference on Image, Video and Signal Processing

February 25 - 28, 2019

Shanghai, China

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Cited By

Tuama SSaud JRashid O(2024)Automatic Identification of Ear Patterns Based on Convolutional Neural NetworkNew Trends in Information and Communications Technology Applications10.1007/978-3-031-62814-6_6(75-89)Online publication date: 26-Jun-2024
https://doi.org/10.1007/978-3-031-62814-6_6
Xu XLiu YLiu CLu L(2023)A Feature Fusion Human Ear Recognition Method Based on Channel Features and Dynamic ConvolutionSymmetry10.3390/sym1507145415:7(1454)Online publication date: 21-Jul-2023
https://doi.org/10.3390/sym15071454
Oyebiyi OAbayomi-Alli AArogundade OQazi AImoize AAwotunde J(2023)A Systematic Literature Review on Human Ear Biometrics: Approaches, Algorithms, and Trend in the Last DecadeInformation10.3390/info1403019214:3(192)Online publication date: 17-Mar-2023
https://doi.org/10.3390/info14030192
Ganapathi IAli SPrakash SVu NWerghi N(2023)A Survey of 3D Ear Recognition TechniquesACM Computing Surveys10.1145/356088455:10(1-36)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3560884
Mahajan ASingla S(2023)Csa-gru: a hybrid CNN and self attention GRU for human identification using ear biometricsEvolving Systems10.1007/s12530-023-09555-415:4(1197-1218)Online publication date: 16-Dec-2023
https://doi.org/10.1007/s12530-023-09555-4
Minaee SAbdolrashidi ASu HBennamoun MZhang D(2023)Biometrics recognition using deep learning: a surveyArtificial Intelligence Review10.1007/s10462-022-10237-x56:8(8647-8695)Online publication date: 13-Jan-2023
https://doi.org/10.1007/s10462-022-10237-x
Lei YQian JPan DXu T(2022)Research on Small Sample Dynamic Human Ear Recognition Based on Deep LearningSensors10.3390/s2205171822:5(1718)Online publication date: 22-Feb-2022
https://doi.org/10.3390/s22051718
Murthy NVrancx PLaubeuf NDebacker PCatthoor FVerhelst M(2022)Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)10.1109/SEC54971.2022.00009(14-25)Online publication date: Dec-2022
https://doi.org/10.1109/SEC54971.2022.00009
Attallah BBrik YNadir C(2022)Ear Recognition Using Ensemble of Deep Features and Machine Learning Classifiers2022 32nd International Conference on Computer Theory and Applications (ICCTA)10.1109/ICCTA58027.2022.10206252(68-73)Online publication date: 17-Dec-2022
https://doi.org/10.1109/ICCTA58027.2022.10206252
Elaraby NBarakat SRezk A(2022)A conditional GAN-based approach for enhancing transfer learning performance in few-shot HCR tasksScientific Reports10.1038/s41598-022-20654-112:1Online publication date: 29-Sep-2022
https://doi.org/10.1038/s41598-022-20654-1
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