Comparative Analysis of VGG-16 and ResNet-50 for Occluded Ear Recognition

Abstract

Occluded ear recognition is a challenging task in biometric systems due to the presence of occlusions that can hinder accurate identification. There is still a research gap in enhancing the robustness of deep learning to handle severities of occlusions with different datasets. This research focuses on developing a robust occluded ear recognition system by implementing fine-tuning techniques on three popular pre-trained deep learning models, Residual Neural Network (ResNet-50), Visual Geometry Group (VGG-16), and EfficientNet. The system is evaluated on two manually occluded ear datasets, which are the AMI ear dataset and the IITD ear dataset. The experiment results showed the fine-tuned ResNet-50 model performs better than the fine-tuned VGG-16 model. The results indicate that the model's ability to accurately predict the classes or labels decreases as more data is occluded. Higher occlusion rates lead to a loss of important information, making it more challenging for the model to distinguish between different patterns and make accurate predictions. According to the findings, the amount of occlusion influenced the identification accuracy and worsened as the occlusion became larger. In the future, ear recognition systems will likely continue to improve in accuracy and be adopted by a wider range of organizations and industries. They may also be integrated with other biometric technologies and used for personalization purposes. However, ethical considerations related to the use of ear recognition systems will also need to be addressed.