Multibiometric System Using Distance Regularized Level Set Method and Particle Swarm Optimization

摘要

This paper presents a multibiometric system that integrates the iris, palmprint, and fingerprint features based on the fusion at feature level. The novelty of this research effort is that we propose a feature subset selection scheme based on Particle Swarm Optimization (PSO) with a new fitness function that minimizes the Recognition Error (RR), False Accept Rate (FAR), and Feature Subset Size (FSS). Furthermore, we apply a Distance Regularized Level Set (DRLS)-based iris segmentation procedure, which maintains the regularity of the level set function intrinsically during the curve evolution process and increases the numerical accuracy substantially. The proposed iris localization scheme is robust against poor localization and weak iris/sclera boundaries. Experimental results indicate that the proposed approach increases biometric recognition accuracies compared to that produced by single modal biometrics.