Optimized multi‐biometric enhancement analysis

Abstract

A multi-biometric system uses different modalities to identify individuals more accurately. The authors analyse fusion efficiency of a significant number of multi-biometric fusion schemes. To do so, the study applies different functions that are generated using genetic programming (GP) on the 2000 multi-biometric instances produced by the fusion of different biometric matching scores. The functions are represented using a tree of arithmetic operations and are used for fusion at score level. First, genetic programming is implemented on the XM2VTS score database. The GP optimizes the half total error rate of fused matching scores. Then, a comparative study is performed based on our experiments on matching scores of different biometric baseline systems provided by the bio-secure database. This database provides 24 streams that we use to generate 2000 multi-biometric combinations. These multi-biometric instances combine matching scores of different instances, sensors and traits. To assess the quality of the fused scores and the quality of performing biometric baseline systems, we use weighted functions based on user-specific and group-specific normalization. Then, we propose a hybrid cat swarm optimization (CSO) based on the average-velocity inertia-weighted CSO and the normal mutation strategy-based CSO to compute the weights of the selected functions for the fused biometric systems. Finally, we present the statistical significance tests to confirm that the proposed functions outperform the existing functions based on arithmetic rules, normalization fusion and evolutionary algorithms.