A New Methodology of Illumination Estimation/Normalization Based on Adaptive Smoothing for Robust Face Recognition

In this paper, we propose a novel method of illumination estimation/normalization based on adaptive smoothing, which is to be applied to robust face recognition. In order to estimate the illumination in the framework of retinex theory, adaptive smoothing is applied based on both iterative convolution and two discontinuity measures. In addition to that, we also introduce a couple of new concepts, which are designed to be suitable especially for face images. One is the new conduction function for adaptive weighting, and the other is the smoothing constraint for more accurate description of real environments. The evaluations, which are conducted based on the Yale face database B, show that the proposed method achieves high recognition rates even in more challenging environments such as the case of using images with the worst case of illumination as a training set.