Multi-classifier Q-stack Aging Model for Adult Face Verification

The influence of age progression on the performance of multi-classifier face verification systems is a challenging and largely open research problem that deserves more and more attention. In this paper, we propose to manage the aging influence on the adult face verification system by a multi-classifier Q-stack age modeling technique, which uses the age as a class-independent metadata quality measure together with scores from baseline classifiers, combining global and local patterns, in order to obtain better recognition rates. 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 and age-dependent 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. The proposed approach is evaluated on the MORPH database.