Face verification across age progression using discriminative methods

Face verification in the presence of age progression is an important problem that has not been widely addressed. In this paper, we study the problem by designing and evaluating discriminative approaches. These directly tackle verification tasks without explicit age modeling, which is a hard problem by itself. First, we find that the gradient orientation (GO), after discarding magnitude information, provides a simple but effective representation for this problem. This representation is further improved when hierarchical information is used, which results in the use of the gradient orientation pyramid (GOP). When combined with a support vector machine (SVM) GOP demonstrates excellent performance in all our experiments, in comparison with seven different approaches including two commercial systems. Our experiments are conducted on the FGnet dataset and two large passport datasets, one of them being the largest ever reported for recognition tasks. Second, taking advantage of these datasets...

• Type: Journal
• Year: 2010
• Where: TIFS
• Authors: Haibin Ling, Stefano Soatto, Narayanan Ramanathan, David W. Jacobs
• Keywords: Age Gaps, Education, Facial Hair, Gradient Orientation, TIFS 2010