Fusing Face and Fingerprint for Identity Authentication by SVM

Abstract

Biometric based person identity authentication is gaining more and more attention. It has been proved that combining multi-biometric modalities enables to achieve better performance than single modality. This paper fused Face and fingerprint (for one identity, face and fingerprint are from the really same person) for person identity authentication, and Support Vector Machine (SVM) is adopted as the fusion strategy. Performances of three SVMs based on three different kernel functions (Polynomial, Radial Based Function and Hyperbolic Tangent) are given out and analyzed in detail. Three different protocols are defined and operated on different data sets. In order to enhance the ability to bear face with bigger pose angle, a client specific SVM classifier is brought forward. Experiment results proved that it can improve the fusion authentication accuracy, and consequently expand the allowable range of face turning degree to some extend in fusion system also.