A method for estimating authentication performance over time, with applications to face biometrics

Abstract

Underlying biometrics are biological tissues that evolve over time. Hence, biometric authentication (and recognition in general) is a dynamic pattern recognition problem. We propose a novel method to track this change for each user, as well as over the whole population of users, given only the system match scores. Estimating this change is challenging because of the paucity of the data, especially the genuine user scores. We overcome this problem by imposing the constraints that the user-specific class-conditional scores take on a particular distribution (Gaussian in our case) and that it is continuous in time. As a result, we can estimate the performance to an arbitrary time precision. Our method compares favorably with the conventional empirically based approach which utilizes a sliding window, and as a result suffers from the dilemma between precision in performance and the time resolution, i.e., higher performance precision entails lower time resolution and vice-versa. Our findings applied to 3D face verification suggest that the overall system performance, i.e., over the whole population of observed users, improves with use initially but then gradually degrades over time. However, the performance of individual users varies dramatically. Indeed, a minority of users actually improve in performance over time. While performance trend is dependent on both the template and the person, our findings on 3D face verification suggest that the person dependency is a much stronger component. This suggests that strategies to reduce performance degradation, e.g., updating a biometric template/model, should be person-dependent. © Springer-Verlag Berlin Heidelberg 2007.