Boosting Accuracy and Transparency in Biometric Finger-Vein Recognition: CNN with Adaptive Interpretable Recognition

Biometric finger-vein recognition is a promising approach for secure and reliable identification of individuals based on the unique patterns present in the veins of their fingers. However, achieving high accuracy, adaptability, and interpretability in finger-vein recognition systems remains a challenge. In this study, we propose a CNN (Convolutional Neural Network) with adaptive interpretable recognition along transfer learning approach to address these challenges. First, we leverage the power of CNNs by employing the Xception model, a state-of-the-art deep learning architecture, as the backbone of our system. The Xception model is pre-trained on large-scale datasets and provides excellent feature extraction capabilities, capturing discriminative finger-vein patterns effectively. To enhance adaptability, we employ transfer learning techniques. By starting with a pre-trained Xception model, we leverage the learned feature representations and fine-tune the model using a smaller labeled dataset specific to finger-vein recognition. This approach reduces the need for a large amount of labeled data and improves the model’s generalization ability. In addition to accuracy, interpretability plays a crucial role in biometric systems. To address this, we integrate interpretable recognition techniques. By combining the power of CNNs, adaptive transfer learning, and interpretable recognition, we achieve high accuracy, adaptability to varying finger-vein patterns, and interpretability for user trust and understanding. This work contributes to the development of robust and transparent biometric recognition systems, with potential applications in security, access control, and identity verification.