Cancelable finger vein authentication using multidimensional scaling based on deep learning

In the field of identity verification and identification, biometrics has evolved as a reliable approach for identifying individuals based on their unique physical or behavioral characteristics. The utilization of finger vein authentication has generated significant attention as a biometric modality owing to its strong resilience, resistance against spoofing attacks, and consistent patterns. In this work, we proposed a novel cancelable finger vein authentication system using multidimensional scaling (MDS) based on deep learning. Our method addressed the limitations of previous biometric authentication systems by integrating MDS with a lightweight convolutional neural network (CNN) model for feature extraction. The cancelable approach ensured privacy and security by generating distinct templates for each user. We evaluated our system on three publicly available datasets for finger veins using various performance metrics, including accuracy, precision, recall, and equal error rate (EER). The results demonstrated the effectiveness of our method, which achieved high accuracy, low error rates, and strong performance in diversity and irreversibility tests. Additionally, our system maintained high authentication accuracy while preserving user privacy, making it suitable for practical applications in biometric authentication.