Face Verification Across Age Progression Using Discriminative Methods

• Published in: IEEE transactions on information forensics and security Vol. 5; no. 1; pp. 82 - 91
• Main Authors: Haibin Ling, Soatto, S., Ramanathan, N., Jacobs, D.W.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.03.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Age; Age progression; Application software; Computer science; Face recognition; face verification; Facial; Gaps; gradient orientation pyramid (GOP); Hair; Image color analysis; Image quality; Image recognition; Jacobian matrices; Progressions; Recognition; Representations; support vector machine (SVM); Support vector machines; Surveillance
• ISSN: 1556-6013; 1556-6021
• DOI: 10.1109/TIFS.2009.2038751

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, 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 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, we empirically study how age gaps and related issues (including image quality, spectacles, and facial hair) affect recognition algorithms. We found surprisingly that the added difficulty of verification produced by age gaps becomes saturated after the gap is larger than four years, for gaps of up to ten years. In addition, we find that image quality and eyewear present more of a challenge than facial hair.