https://jjcit.org/paper/143
UNCONSTRAINED EAR RECOGNITION USING TRANSFORMERS
10.5455/jjcit.71-1627981530
Marwin B. Alejo
Deep learning,Neural networks,Transformers,Vision transformer,Data-efficient image transformers,Ear recognition
8
2804
715
3-Aug.-2021
5-Sep.-2021
12-Sep.-2021
The advantages of the ears as a means of identification over other biometric modalities provided an avenue for
researchers to conduct biometric recognition studies on state-of-the-art computing methods. This paper presents
a deep learning pipeline for unconstrained ear recognition using a transformer neural network: Vision
Transformer (ViT) and Data-efficient image Transformers (DeiTs). The ViT-Ear and DeiT-Ear models of this
study achieved a recognition accuracy comparable or more significant than the results of state-of-the-art CNN-
based methods and other deep learning algorithms. This study also determined that the performance of Vision
Transformer and Data-efficient image Transformer models works better than that of ResNets without using
exhaustive data augmentation processes. Moreover, this study observed that the performance of ViT-Ear is
nearly like that of other ViT-based biometric studies.