An illumination normalization model for face recognition under varied lighting conditions

In this paper, a novel illumination normalization model is proposed for the pre-processing of face recognition under varied lighting conditions. The novel model could compensate all the illumination effects in face samples, like the diffuse reflection, specular reflection, attached shadow and cast shadow. Firstly, it uses the TV_L-1 model to get the low-frequency part of face image, and adopts the self-quotient model to normalize the diffuse reflection and attached shadow. Then it generates the illumination invariant small-scale part of face sample. Secondly, TV_L-2 model is used to get the noiseless large-scale part of face sample. All kinds of illumination effects in the large-scale part are further removed by the region-based histogram equalization. Thirdly, two parts are fused to generate the illumination invariant face sample. The result of our model contains multi-scaled image information, and all illumination effects in face samples are compensated. Finally, high-order statistical relationships among variables of samples are extracted for classifier. Experimental results on some large scale face databases prove that the processed image by our model could largely improve the recognition performances of conventional methods under low-level lighting conditions. (C) 2010 Elsevier B.V. All rights reserved.