In this paper a novel multi-scale preprocessing model (MSPM) for face recognition is proposed. MSPM removes lighting effects and enhances the image feature in two scales simultaneously. It decomposes the original image using Total Variation model. Then the lighting effects are normalized by self-quotient in the small scale part and equalized in the large scale part. The final fused image is illumination invariant. Using this image could largely improve face recognition performance under low-level lighting conditions. Combined MSPM with high-order Gabor-based methods could further raise face recognition rates under varying imaging conditions. According to the experiments on the large scale CAS-PEAL face database, MSPM outperforms conventional algorithms when they face most artifacts (lighting, expression, masking etc.).
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