Adaptive Client-Impostor Centric Score Normalization: A Case Study in Fingerprint Verification

Cohort-based score normalization as exemplified by the T-norm (for Test normalization) has been the state-of the-art approach to account for the variability of signal quality in testing. On the other hand, user-specific score normalization such as the Z-norm and the F-norm, designed to handle variability in performance across different reference models, has also been shown to be very effective. Exploiting the strength of both approaches, this paper proposes a novel score normalization called adaptive F-norm, which is client-impostor centric, i.e., utilizing both the genuine and impostor score information, as well as adaptive, i.e., adaptive to the test condition thanks to the use of a pool of cohort models.