Person Identification by Models Trained Using Left and Right Ear Images Independently
K. R. Resmi, G. Raju, Vijaya Padmanabha, Joseph P. Mani · 2023
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.