Retinex method based on adaptive smoothing for illumination invariant face recognition

In this paper, we propose the Retinex method for illumination invariant face recognition developed on the basis of adaptive smoothing technology. By the well-known Retinex theory, illumination is generally estimated and normalized by smoothing the input image first and then dividing the estimate into the original input image. Therefore, performance mainly depends on how good the estimated illumination is. The proposed method estimates illumination by iteratively convolving the input image with a 3×33×3 smoothing mask weighted by a coefficient via combining two measures of the illumination discontinuity at each pixel. We address a couple of additional concepts, which are designed to be suitable especially for face images. One is the new conduction function for adaptive smoothing, and the other is the smoothing constraint for more accurate description of real environments. In this way, we can achieve an efficient illumination normalization in which face images with even strong shadows are normalized efficiently. The proposed method is evaluated based on Yale face database B, CMU PIE database and AR face database by applying PCA. The comparative results indicate that the proposed method present consistent and promising results even when images under harsh illumination conditions are used as a training set.