Extracting Discriminative Information From Cohort Models

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Executive Summary

Cohort models are non-match models available in a biometric system. They could be other enrolled models in the gallery of the system. Cohort models have been widely used in biometric systems. A well-established scheme such as T-norm exploits cohort models to predict the statistical parameters of non-match scores for biometric authentication. They have also been used to predict failure or recognition performance of biometric system. In this paper the authors show that cohort models that are sorted by their similarity to the claimed target model, can produce a discriminative score pattern.

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