Deep Learning Approaches for Ear Biometrics: A Novel Approach

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This paper presents a novel deep learning model for ear biometrics, achieving state-of-the-art performance through the integration of transfer learning and data augmentation. Ear biometrics has garnered significant interest due to the ear's unique and stable characteristics, making it a viable modality for biometric identification. Traditional methods often falter under variations in lighting, pose, and occlusion, but deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown exceptional capability in overcoming these challenges by learning robust and discriminative features. Our comprehensive review explores the application of CNNs, Recurrent Neural Networks (RNNs), transfer learning, attention mechanisms, and multimodal fusion in ear recognition, verification, and identification tasks. Our model demonstrates remarkable recognition and verification accuracy, underscoring the potential of ear images as a reliable biometric modality. Experimental results show a recognition rate of 99.2% and an equal error rate (EER) of 0.8%, highlighting the effectiveness of our approach in real-world scenarios. Future work will involve expanding the dataset, exploring alternative deep learning architectures, and enhancing the robustness of ear biometric systems against security threats. Key Words: Ear Biometrics, Deep Learning, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Attention Mechanisms, Multimodal Fusion.