Face recognition under arbitrary illumination using illuminated exemplars

Recently, the importance of face recognition has been increasingly emphasized since popular CCD cameras are distributed to various applications. However, facial images are dramatically changed by lighting variations, so that facial appearance changes caused serious performance degradation in face recognition. Many researchers have tried to overcome these illumination problems using diverse approaches, which have required a multiple registered images per person or the prior knowledge of lighting conditions. In this paper, we propose a new method for face recognition under arbitrary lighting conditions, given only a single registered image and training data under unknown illuminations. Our proposed method is based on the illuminated exemplars which are synthesized from photometric stereo images of training data. The linear combination of illuminated exemplars can represent the new face and the weighted coefficients of those illuminated exemplars are used as identity signature. We make experiments for verifying our approach and compare it with two traditional approaches. As a result, higher recognition rates are reported in these experiments using the illumination subset of Max-Planck Institute face database and Korean face database.