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Every person is unique. This uniqueness is not only prevalent in his/her biometric traits, but also in the way he/she interacts with a biometric device. A recent trend in tailoring a biometric system to each user (client) is by normalizing the match score for each claimed identity. This technique is called user- (or client-) specific score normalization. This concept can naturally be extended to the multimodal biometrics where several biometric devices and/or traits are involved. This chapter gives a survey on user-specific score normalization as well as compares several representative techniques in this research direction. It also shows how this technique can be used for designing an effective user-specific fusion classifier.
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