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Research Article Open Access
Performance of the face verification system depends on many conditions. One of the most problematic is varying illumination condition. Making recognition more reliable under uncontrolled lighting conditions is one of the most important challenges for practical face recognition systems. Our paper presents a simple and efficient preprocessing method that eliminates most of the effects of changing illumination and shadows while still preserving the essential appearance details that are needed for recognition. This preprocessing method run before feature extraction that incorporates a series of stages designed to counter the effects of illumination variations, local shadowing, and highlights while preserving the essential elements of visual appearance. In this paper, proposed a robust Face Recognition System under uncontrolled illumination variation. In this Face recognition system consists of three phases, illumination insensitive preprocessing method, Feature-extraction and score fusion. In the preprocessing stage illumination sensitive image transformed into illumination-insensitive image, and then to combines multiple classifiers with complementary features instead of improving the accuracy of a single classifier. Score fusion computes a weighted sum of scores, where the weight is am measure of the discriminating power of the component classifier. In this system demonstrated successful accuracy in face recognition under different illumination condition. The method provides good performance on three sets that are widely used for testing under difficult lighting conditions: Extended Yale-B, Face Recognition Grand Challenge Version 2 experiment (FRGC- 204), FERET datasets. The results obtained from the experiments showed that the illumination preprocessing methods significantly improves the recognition rate and it’s a very important step in face verification system.