Person Identification by Models Trained Using Left and Right Ear Images Independently

DOI

10.2991/978-94-6463-110-4_20How to use a DOI?

Keywords

Ear Recognition; Deep Learning; ResNet50; Occlusion

Abstract

The application of Deep Learning Techniques in biometrics has grown significantly during the last decade. The use of deep learning models in ear biometrics is restricted due to the lack of large ear datasets. Researchers employ transfer learning based on several pretrained models to overcome the limitations. For the unconstrained AWE ear dataset, traditional Machine Learning (ML) techniques and hand-crafted features fall short of providing a good recognition accuracy. This paper evaluates the influence of separating left and right ears and the effect of occlusion on the recognition accuracy in AWE dataset. The left and right ear of a person need not be identical. A study by separating the left and right ear into two different datasets is carried out with the pretrained ResNet50 based model. There is a remarkable increase in accuracy when the left and right ear images are independently considered. A new data augmentation technique, incorporating occlusion, is also proposed and experimented with the ResNet50 based model.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - K. R. Resmi
AU  - G. Raju
AU  - Vijaya Padmanabha
AU  - Joseph Mani
PY  - 2023
DA  - 2023/01/30
TI  - Person Identification by Models Trained Using Left and Right Ear Images Independently
BT  - Proceedings of the 1st International Conference on Innovation in Information Technology and Business (ICIITB 2022)
PB  - Atlantis Press
SP  - 281
EP  - 288
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-110-4_20
DO  - 10.2991/978-94-6463-110-4_20
ID  - Resmi2023
ER  -