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A comprehensive survey and deep learning-based approach for human recognition using ear biometric.

Authors
  • Kamboj, Aman1
  • Rani, Rajneesh1
  • Nigam, Aditya2
  • 1 National Institute of Technology Jalandhar, Jalandhar, Punjab 144011 India. , (India)
  • 2 Indian Institute of Technology Mandi, Mandi, Himachal Pradesh 175005 India. , (India)
Type
Published Article
Journal
The Visual computer
Publication Date
Apr 22, 2021
Pages
1–34
Identifiers
DOI: 10.1007/s00371-021-02119-0
PMID: 33907343
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Human recognition systems based on biometrics are much in demand due to increasing concerns of security and privacy. The human ear is unique and useful for recognition. It offers numerous advantages over popular biometrics traits face, iris, and fingerprints. A lot of work has been attributed to ear biometric, and the existing methods have achieved remarkable success over constrained databases. However, in unconstrained environment, a significant level of difficulty is observed as the images experience various challenges. In this paper, we first have provided a comprehensive survey on ear biometric using a novel taxonomy. The survey includes in-depth details of databases, performance evaluation parameters, and existing approaches. We have introduced a new database, NITJEW, for evaluation of unconstrained ear detection and recognition. A modified deep learning models Faster-RCNN and VGG-19 are used for ear detection and ear recognition tasks, respectively. The benchmark comparative assessment of our database is performed with six existing popular databases. Lastly, we have provided insight into open-ended research problems worth examining in the near future. We hope that our work will be a stepping stone for new researchers in ear biometrics and helpful for further development. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

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