Deep Learning Techniques to enhance Biometric Authentication using Hand Features

Abstract

This research provides an extensive analysis of the integration of palm vein and palm print features as multimodal biometrics to enhance secure authentication. The use of palm vein and palm print identification has become more popular owing to its exceptional precision and non-invasive characteristics. Nevertheless, each modality has its own distinct advantages and disadvantages. In order to address these constraints, researchers have suggested many approaches for fusion palm vein and palm print features. This article examines contemporary research in this domain, including the utilization of deep learning methodologies. It discusses the challenges in palm vein and palm print recognition and explores the potential of deep learning methods to address these challenges. The proposed fusion technique combines feature-level fusion with score-level fusion, resulting in a more accurate and secure biometric authentication system. Experimental results demonstrate the effectiveness of the proposed approach, showing significant improvements in recognition accuracy. A Genuine Accept Rate (GAR) of 98.3% and an Equal Error Rate (EER) of 2.5% are achieved by the Long Short-Term Memory (LSTM) algorithm. This makes it better than deep learning algorithms like Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Deep Belief Networks (DBN). The proposed fusion technique also achieves a low False Accept Rate (FAR) of 1.7%. These results highlight the superior performance of the fusion approach in biometric recognition scenarios. Future research directions are discussed to further enhance the performance of palm vein and palm print recognition systems.