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Rank level fusion of multimodal biometrics using genetic algorithm

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Abstract

Multimodal biometric systems are highly used over unimodal biometric systems. The multimodal systems fuse information from multiple biometric traits to overcome the limitations, like, inter-class similarities, non-universality of unimodal biometric systems. This fusion significantly enhances the overall performance of the biometric systems. One of the ways of fusing information for multimodal biometrics is rank level fusion. In this paper, rank level fusion is formulated as an optimization problem. A novel genetic algorithm (GA) based method is proposed for rank level fusion of multimodal biometrics. It minimizes the distances between an aggregated rank list and each input rank list being derived from individual biometric trait. The proposed method uses Spearman footrule distance measure to find the said distance between a pair of rank lists. Superiority of the proposed method over several existing rank level and score level fusion methods is demonstrated experimentally.

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Authors and Affiliations

  1. University of Hyderabad, Hyderabad, India

    Shadab Ahmad & Avatharam Ganivada

  2. Institute for Development and Research in Banking Technology, Hyderabad, India

    Shadab Ahmad & Rajarshi Pal

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  1. Shadab Ahmad
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Ahmad, S., Pal, R. & Ganivada, A. Rank level fusion of multimodal biometrics using genetic algorithm. Multimed Tools Appl 81, 40931–40958 (2022). https://doi.org/10.1007/s11042-022-12688-4

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