https://jjcit.org/paper/143 10.5455/jjcit.71-1627981530 Marwin B. Alejo Deep learning,Neural networks,Transformers,Vision transformer,Data-efficient image transformers,Ear recognition 16 5482 1222 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.