Face verification across age progression using discriminative methods

  • Authors:

  • Haibin Ling;Stefano Soatto;Narayanan Ramanathan;David W. Jacobs

  • Affiliations:

  • Department of Computer and Information Science, Temple University, Philadelphia, PA;Computer Science Department, University of California, Los Angeles, CA;Cernium Corporation, Reston, VA;Computer Science Department, University of Maryland, College Park, MD

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2010

Quantified Score

Hi-index 0.00

Visualization

Abstract

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.