Human ear print recognition using deep learning techniques

Abstract

Ear biometrics has risen as a promising methodology for human identification, due to the ear’s uniqueness, permanence, and constrained variability over time. This thesis centers on enhancing ear recognition systems utilizing deep learning techniques, particularly by leveraging convolutional neural networks. The proposed approach includes implementing and fine-tuning the ResNet50 architecture utilizing the AMI Ear dataset, which contains ear images captured under diverse conditions and postures. Data preprocessing and augmentation methods are applied to enhance model robustness, and training is conducted with careful tuning to adapt to the specific characteristics of ear pictures. The experimental results demonstrate that the proposed ResNet50-based model achieves improved recognition performance, reaching an accuracy of 99.29%, outperforming previous methods. This work demonstrates the potential for deep convolutional models in ear biometrics and sets the stage for future applications of multi-view fusion, large datasets, and combined modalities with other biometric traits.