2-layer Parallel SVM Network Based on Aggregated Local Descriptors for Fingerprint Liveness Detection

Abstract

Fingerprint liveness detection is an effective way to ensure the security and reliability of fingerprint recognition algorithms against spoof fingerprint attacks. Local descriptors are one of the most widely studied fingerprint liveness detection algorithms. However, the performance of simplex local descriptors or simple voting models among multiple descriptors still can-not achieve satisfactory accuracy, robustness, and applicability. This paper proposes a 2-layer parallel Support Vector Machine (SVM) network to improve the classification performance of local descriptors and achieve 95.32% accuracy on the LivDet datasets (2009, 2011, 2013, and 2015). The experimental results and theoretical analysis indicate that the proposed 2-layer parallel SVM network based on aggregated local descriptors shows better detection accuracy and model robustness against adversarial attacks compared with simplex descriptors and state-of-the-art neural network structures. Besides, the 2-layer parallel SVM network can save training time through parallel computing, and achieve extremely high accuracy and reliability through ultra-high-dimensional descriptor classification.