Multimodal Biometric Fusion Using Evolutionary Techniques

Abstract

Biometrics refers to the automatic recognition of the person based on his physiological or behavioral characteristics, such as fingerprint, face, voice, gait …etc. However, Unimodal biometric system suffers from several limitations, such as non-universality and susceptibility to spoof attacks. To alleviate this problems, information from different biometric sources are combined and such systems are known as multimodal biometric systems. In this thesis, we propose Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) as two evolutionary techniques to combine face and voice modalities at the matching scores level. The effectiveness of these two techniques is compared to those obtained by using a simple BFS, a hybrid intelligent (ANFIS) and a statistical learning (SVM) fusion techniques. The wellknown Min-Max normalization technique is used to transform the individual matching scores into a common range before the fusion can take place. The proposed schemes are experimentally evaluated on publicly available datasets of scores (XM2VTS, TIMIT, NIST and BANCA) and under three different data quality conditions namely, clean varied and degraded. In order to reduce the effects of scores variations on the accuracy of biometric systems, we use Unconstraint Cohort Normalization (UCN) mechanism to normalize the matching scores before combining them. It is revealed in this study that by deploying such fusion techniques, the verification error rates (EERs) can be reduced considerably, and subjecting the scores to UCN process before combining them has resulted in reducing the verification EERs for the single modalities as well as for multimodal biometric fusion.