Computer and Information Sciences - Publications

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Browsing Computer and Information Sciences - Publications by Author "Ahmad, Shadab"
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    Rank Level Fusion of Multimodal Biometrics Based on Cross-Entropy Monte Carlo Method
    ( 2020-01-01) Ahmad, Shadab ; Pal, Rajarshi ; Ganivada, Avatharam
    In unimodal biometric systems, there are several limitations like, non-universality, noisy data and other security risks. To overcome these, multimodal biometric systems are increasingly adopted. Multimodal biometric systems fuse information from multiple biometric traits. Rank level fusion is one of the approaches of information fusion for multimodal biometrics. In this paper, rank level fusion is considered as an optimization problem. Its aim is to minimize the distances between an aggregated rank list and each input rank list from individual biometric trait. A solution of this optimization problem has been proposed using cross-entropy (CE) Monte Carlo method. The proposed CE method uses two distance measures - namely, Spearman footrule and Kendall’s tau distances. Superiority of the proposed CE method based on above two distance measures over several existing rank level and score level fusion schemes is achieved on two different datasets.
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    Score Level Fusion of Multimodal Biometrics Using Genetic Algorithm
    ( 2021-01-01) Ahmad, Shadab ; Pal, Rajarshi ; Ganivada, Avatharam
    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.