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An Improved Score Level Fusion in Multimodal Biometric Systems

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6 Author(s)
Shi-Jinn Horng ; Dept. of Comput. Sci. & Inf. Eng., Nat. Taiwan Univ. of Sci. & Technol., Taipei, Taiwan ; Yuan-Hsin Chen ; Run, Ray-Shine ; Rong-Jian Chen
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In a multimodal biometric system, the effective fusion method is necessary for combining information from various single modality systems. In this paper we examined the performance of sum rule-based score level fusion and support vector machines (SVM)-based score level fusion. Three biometric characteristics were 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.

Published in:

Parallel and Distributed Computing, Applications and Technologies, 2009 International Conference on

Date of Conference:

8-11 Dec. 2009