Adaptive client-impostor centric score normalization: A case study in fingerprint verification

Cohort-based score normalization as examplified 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 strenghth 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. Experiments based on the biosecure DS2 database which contains 6 fingers of 415 subjects, each acquired using a thermal and an optical device, show that the proposed adaptive F-norm is better or at least as good as the other alternatives, including those recently proposed in the literature.