A Novel Approach for Multimodal Biometric Score Fusion Using Gaussian Mixture Model and Monte Carlo Method

Multimodal biometric fusion is gaining more attraction among researchers. As multimodal biometric consolidates the information from multiple biometric sources, the effective fusion of information obtained at score level is a challenging task. In this paper, we propose a novel frame work for optimal combination of match scores using Gaussian mixture model (GMM) and Monte Carlo method. The proposed fusion approach has the ability to handle 1) small size of match scores as is more commonly encountered in biometric fusion and 2) arbitrary distribution of match scores. The proposed fusion scheme is compared with more robust fusion schemes such as SUM rule, weighted SUM rule, Fishers linear discriminate analysis (FLD) and likelihood ratio (LR) method. Extensive experiments are carried out on three different build multimodal biometric databases. Experimental results indicate that proposed fusion scheme achieves higher performance as compared with other fusion techniques.