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. The authors 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. They overcome this problem by imposing the constraints that the user-specific class-conditional scores take on a particular distribution and that it is continuous in time. As a result, they can estimate the performance to an arbitrary time precision.