A Method for Estimating Authentication Performance over Time, with Applications to Face Biometrics

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 findin...

• Type: Conference
• Year: 2007
• Where: CIARP
• Authors: Norman Poh, Josef Kittler, Raymond S. Smith, Jose Rafael Tena
• Keywords: Arbitrary Time Precision, CIARP 2007, Performance, Time Resolution