Department of Computer Science and Information Systems

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Subject: search.filters.subject.Multimodal biometrics

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    Error Level Fusion of Multimodal Biometrics
    (JPRR, 2011) Grover, Jyotsana
    This paper presents a multimodal biometric system based on error level fusion. Two error level fusion strategies, one involving the Choquet integral and another involving the t-norms are proposed. The first strategy fully exploits the non additive aspect of the integral that accounts for the dependence or the overlapping information between the error rates FAR's and FRR's of each biometric modality under consideration. A hybrid learning algorithm using combination of Particle Swarm Optimization, Bacterial Foraging and Reinforcement learning is developed to learn the fuzzy densities and the interaction factor. The second strategy employs t-norms that require no learning. The fusion of the error rates using t-norms is not only fast but results in very good performance. This sort of fusion is a kind of decision level fusion as the error rates are derived from the decisions made on individual modalities. The experimental evaluation on two hand based datasets and two publically available datasets confirms the utility of the error level fusion
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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).