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Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/15767

Title: Performance Evaluation of Score Level Fusion in Multimodal Biometric Systems
Authors: Muhammad Khurram Khan
Keywords: Multimodal biometrics
Issue Date: 2010
Publisher: Journal of Pattern Recognition
Abstract: In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion are examined. Three biometric characteristics are considered in this study: fingerprint, face, and finger vein. We also proposed a new robust normalization scheme (Reduction of High-scores Effect normalization) which is derived from min-max normalization scheme. Experiments on four different multimodal databases suggest that integrating the proposed scheme in sum rule-based fusion and SVM-based fusion leads to consistently high accuracy. The performance of simple sum rule-based fusion preceded by our normalization scheme is comparable to another approach, likelihood ratio-based fusion [8] (Nandakumar et al., 2008), which is based on the estimation of matching scores densities. Comparison between experimental results on sum rule-based fusion and SVM-based fusion reveals that the latter could attain better performance than the former, provided that the kernel and its parameters have been carefully selected.
URI: http://hdl.handle.net/123456789/15767
Appears in Collections:College of Computer and Information Sciences

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