Quo vadis Face Recognition?

Abstract

Within the past decade, major advances have occurred in face recognition. Many systems have emerged that are capable of achieving recognition rates in excess of 90% accuracy under controlled conditions. In field settings, face images are subject to a wide range of variation that includes viewing, illumination, occlusion, facial expression, time delay between acquisition of gallery and probe images, and individual differences. The scalability of face recognition systems to such factors is not well understood. We quantified the influence of these factors, individually and in combination, on face recognition algorithms that included Eigenfaces, Fisherfaces, and FaceIt. Image data consisted of over 37,000 images from 3 publicly available databases that systematically vary in multiple factors individually and in combination: CMU PIE, Cohn-Kanade, and AR databases. Our main findings are: 1) pose variations beyond head rotation substantially depressed recognition rate, 2) time delay: pictures taken on different days but under the same pose and lighting condition produced a consistent reduction in recognition rate, 3) with some notable exceptions, algorithms were robust to variation in facial expression, but not to occlusion. We also found small but significant differences related to gender, which suggests that greater attention be paid to individual differences in future research. Algorithm performance across a range of conditions was higher for women than for men.