Combination of age and head pose for adult face verification

Age progression and other sources of variations such as head pose change have adverse effects on the performance of face verification systems. In this paper, we propose to manage the influence of both the age progression and head pose change on the adult face verification system by a general Q-stack age modeling technique, which uses the age and head pose as class-independent quality measures together with the scores from baseline classifiers, in order to obtain better verification performance. This allows for improved long-term class separation by introducing a 2D parameterized decision boundary in the scores-age space using a short-term enrollment model. This new method, based on the concept of classifier stacking with age- and head pose aware decision boundary compares favorably with the conventional face verification approach, which uses age- and head-pose-independent decision threshold calculated only in the score space at the time of enrollment. In this paper, we also show the advantages of user-specific approach for the face verification task over user-independent approach. The proposed approach is evaluated on the MORPH database.