Multibiometric system using level set method and particle swarm optimization

Abstract

Multibiometric systems alleviate some of the drawbacks possessed by the single modal biometric trait and provide better recognition accuracy. This paper presents a multimodal system that integrates the iris, face, and gait features based on the fusion at feature level. The novelty of this research effort is that a feature subset selection scheme based on Particle Swarm Optimization (PSO) is proposed to select the optimal subset of features from the fused feature vector. In addition, we apply a Variational Level Set (VLS)-based curve evolution scheme to detect the silhouette shape structure. Experimental results indicate that the proposed approach increases biometric recognition accuracies compared to that produced by single modal biometrics. © 2012 Springer-Verlag.