Abstract
In this paper, we propose a novel method of illumination normalization developed on the basis of the retinex theory. In retinex based methods, illumination is generally estimated and normalized by first smoothing the input image and then dividing the estimate into the original input image. The proposed method estimates illumination by iteratively convolving the input image with a 3×3 averaging mask weighted by an efficient measure of the illumination discontinuity at each pixel. In this way, we could achieve a fast illumination normalization in which even face images with strong shadows are normalized efficiently. The proposed method has been evaluated based on the Yale face database B and the CMU PIE database by using PCA. Carrying out various scenarios of test, we have found that our method presented consistent and promising results even when we used images with the worst case of illumination as training sets. We believe that the proposed method has a great potential to be applied to real time face recognition systems, especially under harsh illumination conditions.
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