Score Level Fusion of Palmprint, Face and Iris Using Adaptive PSO

Abstract

Systems that use unimodal biometrics often suffer from various drawbacks such as noise in sensed data, variations that are due to intra class, nonuniversality, spoof attacks, restricted degrees of freedom and high error rates. These limitations can be solved effectively by combining two or more biometric modalities. In this article, a multimodal biometric fusion system is presented that combines palmprint, face and iris traits. The biometric fusion is performed at the score level in order to improve the accuracy of the system. Scores obtained from the three classifiers are fused using adaptive particle swarm optimization (PSO). The PSO use new multi objective fitness function. This function has two objectives, improve the recognition rate and reduce the total equal error rates. The experimental results of the proposed method achieve a recognition accuracy of 100%, with EER of 0.00%, using Gabor filter combined with dimensionality reduction techniques PCA, LDA and KFA. Experimental results show that multimodal biometric systems are much more accurate than the unimodal counterparts.