Deep Cancelable Multibiometric Finger Vein and Fingerprint Authentication With Non-Negative Matrix Factorization

Abstract

Biometric authentication technologies, which use physiological and behavioral traits to verify identity, have added a new layer of protection. However, concerns about privacy, security, and illegal entry persist. Therefore, multibiometric systems have emerged to improve authentication accuracy and system robustness. However, to address these problems more effectively, it is imperative to incorporate cancelable biometrics into multimodal systems. This integration is essential because it offers enhanced security measures and safeguards privacy. This paper presents a novel cancelable multibiometric method that utilizes non-negative matrix factorization (NMF) and a lightweight deep learning model to augment the levels of security and privacy in biometric authentication. The proposed system leverages the distinct capabilities of finger vein and fingerprint recognition modalities to enhance authentication accuracy and improve resistance against spoofing attacks. The implementation of cancelable biometrics principles serves to safeguard user privacy and enhance security measures. NMF is employed to facilitate the extraction of features and the obfuscation of data. The study presents actual evidence to support the system’s high validation results with an average accuracy of 95.31% for the NUPT-FPV dataset and 92.71% for the FVC2004 and FV-USM datasets, its ability to preserve anonymity, and its real-time capabilities. Significantly, it satisfies all conditions that can be canceled, representing a noteworthy progression in the domain of cancelable multibiometric. The amalgamation of NMF with a streamlined deep learning model presents a pragmatic and effective resolution, surmounting prior constraints in computational intricacy.

Keywords

• Cancelable multibiometric
• deep learning
• non-negative matrix factorization
• finger vein
• fingerprint
• authentication