Extracting discriminative information from cohort models

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 we show that cohort models that are sorted by their similarity to the claimed target model, can produce a discriminative score pattern. We also show that polynomial regression can be used to extract discriminative parameters from these patterns. These parameters can be combined with the raw score to improve the recognition performance of an authentication system. The experimental results obtained for the face and fingerprint modalities of the Biosecure database validate this claim.