This paper deals with the influence of age progression on the performance of face verification systems. This is a challenging and largely open research problem that deserves more and more attention. Aging affects both the shape of the face and its texture, leading to a failure in the face verification task. In this paper, the aging influence on the face verification system using local ternary patterns is managed by a Q-stack aging model, which uses the age as a class-independent meta-data quality measure together with baseline classifier scores in order to obtain better recognition rates. This allows for increased long-term class separation by a decision boundary in the score-quality measure space using a short-term enrollment model. This new method, based on the concept of classifier stacking, compares favorably with the conventional face verification approach which uses decision boundary calculated only in the score space at the time of enrollment.