A Study of Face Recognition as People Age

Abstract

In this paper we study face recognition across ages within a real passport photo verification task. First, we propose using the gradient orientation pyramid for this task. Discarding the gradient magnitude and utilizing hierarchical techniques, we found that the new descriptor yields a robust and discriminative representation. With the proposed descriptor, we model face verification as a two-class problem and use a support vector machine as a classifier. The approach is applied to two passport data sets containing more than 1,800 image pairs from each person with large age differences. Although simple, our approach outperforms previously tested Bayesian technique and other descriptors, including the intensity difference and gradient with magnitude. In addition, it works as well as two commercial systems. Second, for the first time, we empirically study how age differences affect recognition performance. Our experiments show that, although the aging process adds difficulty to the recognition task, it does not surpass illumination or expression as a confounding factor.