User-specific Score Normalization and Fusion for Biometric Person Recognition

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. The advantage of this approach, compared to the direct design of such a fusion classifier, is that much less genuine data is needed. Several potential research directions are also outlined.

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