Client-specific A-stack model for adult face verification across aging

The problem of time validity of biometric models has received only a marginal attention from researchers. In this paper, we propose to manage the aging influence on the adult face verification system by an A-stack age modeling technique, which uses the age as a class-independent metadata quality measure together with scores from a single or multiple baseline classifiers, in order to obtain better face verification performance. This allows for improved long-term class separation by introducing a dynamically changing decision boundary across the age progression in the scores-age space using a short-term enrollment model. This new method, based on the concept of classifier stacking and age-aware decision boundary, compares favorably with the conventional face verification approach, which uses age-independent decision threshold calculated only in the score space at the time of enrollment. Our experiments on the YouTube and MORPH data show that the use of the proposed approach allows for improving the identification accuracy as opposed to the baseline classifier.