Cross-Age Face Recognition on a Very Large Database: The Performance versus Age Intervals and Improvement Using Soft Biometric Traits

Facial aging can degrade the face recognition performance dramatically. Traditional face recognition studies focus on dealing with pose, illumination, and expression (PIE) changes. Considering a large span of age difference, the influence of facial aging could be very significant compared to the PIE variations. How big the aging influence could be? What is the relation between recognition accuracy and age intervals? Can soft biometrics be used to improve the face recognition performance under age variations? In this paper we address all these issues. First, we investigate the face recognition performance degradation with respect to age intervals between the probe and gallery images on a very large database which contains about 55,000 face images of more than 13,000 individuals. Second, we study if soft biometric traits, e.g., race, gender, height, and weight, could be used to improve the cross-age face recognition accuracies, and how useful each of them could be.