Department of Computer Science and Information Systems

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Subject: search.filters.subject.Score level fusion

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    Score level fusion of multimodal biometrics using triangular norms
    (Elsevier, 2011-10) Grover, Jyotsana
    A multimodal biometric system that alleviates the limitations of the unimodal biometric systems by fusing the information from the respective biometric sources is developed. A general approach is proposed for the fusion at score level by combining the scores from multiple biometrics using triangular norms (t-norms) due to Hamacher, Yager, Frank, Schweizer and Sklar, and Einstein product. This study aims at tapping the potential of t-norms for multimodal biometrics. The proposed approach renders very good performance as it is quite computationally fast and outperforms the score level fusion using the combination approach (min, mean, and sum) and classification approaches like SVM, logistic linear regression, MLP, etc. The experimental evaluation on three databases confirms the effectiveness of score level fusion using t-norms.
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    Hybrid fusion of score level and adaptive fuzzy decision level fusions for the finger-knuckle-print based authentication
    (Elsevier, 2015-06) Grover, Jyotsana
    This paper presents the hybrid of the adaptive fuzzy decision level fusion and the score level fusion for finger-knuckle-print (FKP) based authentication to improve over the individual fusion methods. The scores obtained from the fusion of the left index (LI) and the left middle (LM) and those obtained from the fusion of the right index (RI) and the right middle (RM) FKP are fused at the fuzzy decision level. The uncertainty in the local decisions made by the individual score level fusion methods is addressed by treating the error rates as fuzzy sets. The operating points (thresholds) are adapted to accommodate the varying the cost of false acceptance rate using the hybrid PSO algorithm that ensures the desired level of security. The error rates associated with the operating points are converted into the fuzzy domain by triangular membership functions and the alpha-cuts are applied on the membership functions for the better representation of uncertainty. The global fuzzy error rates are defuzzified using total distance criterion (TDC). The rigorous experimental results indicate that the hybrid fusion is superior to the component level fusion methods (score level and decision level fusion).