User-Specific Cohort Selection and Score Normalization for Biometric Systems
An increasing body of evidence suggests that cohort-based score normalization can improve the performance of biometric authentication. This approach relies on the use of N cohort biometric templates, which can be computationally expensive. We contribute to the advancement of cohort score normalization in two ways. First, we show both theoretically and empirically that the most similar and the most dissimilar cohort templates to a target user contain discriminative information. We then investigate the extraction of this information using polynomial regression. Extensive evaluation on the face and fingerprint modalities in the Biosecure DS2 dataset indicates that the proposed method outperforms the state-of-the-art cohort score normalization methods, while reducing the computation cost by as much as half.