Baseline face recognition using photometric stereo data

Abstract This research is motivated by the need for face recognition in uncontrolled environments. In other words, we are interested in face recognition arrangements whereby the users do not need to interact with the recognition technology. The contribution of this paper is to perform a range of recognition experiments on face image data as people casually enter a building, without any instructions about expression. Specifically, we capture four images per session in rapid succession (all within 20 ms). The four images are synchronised to different light sources to enable photometric stereo processing to estimate albedo images, surface normals and depth maps. Additional capture sessions then take place over periods of many weeks. Our recognition experiments are on each of the three modalities as well as a fusion technique for the albedo and depth. Using a variety of photometric stereo methods, surface integration methods (to recover depth) and recognition algorithms such as principal component analysis and nonnegative matrix factorisation, we acquire a maximum recognition rate of 86% for 96 subjects.