Face illumination compensation dictionary

Face images captured under distinct lighting conditions have totally different overall appearances, which greatly degrade the recognition accuracy. In this paper, an illumination compensation strategy is worked out to assist linear representation based face recognition. In the past few years, linear representation based face recognition approaches such as SRC and CRC_RLS attract great attention, but their effectiveness greatly depends on a large number of training samples, which seriously restricts their application values. We will illustrate that face illumination distinction could be compensated just through a general linear dictionary, and after enrolling our illumination compensation strategy, even there is only single gallery image for each subject, linear representation recognition approaches can still be relatively robust to probe illumination variance. The proposed strategy is experimented on the Extended Yale B and CMU PIE database.