Score Level Fusion of Multimodal Biometrics Using Genetic Algorithm

Multimodal biometric system fuses information from multiple biometric modalities to overcome limitations of unimodal biometric system. This fusion significantly enhances the performance of the system. One of the ways of fusing information for multimodal biometrics is score level fusion. In this paper, a novel score level fusion method is proposed. Here, fusion at score level is formulated as an optimization problem. The paper proposes a genetic algorithm (GA) based approach to solve this optimization problem. It minimizes the distances between an aggregated score list and each input score list from individual biometric modality. The proposed GA based method uses weighted Spearman footrule distance metric to compute the distance between a pair of score lists. Superiority of the proposed method over several state-of-the-art score level and rank level fusion methods is demonstrated experimentally.