An illumination normalization method for face recognition has been developed since it was difficult to control lighting conditions efficiently in the practical applications. Considering that the irradiation light is of little variation in a certain area, a mean estimation method is used to simulate the illumination component of a face image. Illumination component is removed by subtracting the mean estimation from the original image. In order to highlight face texture features and suppress the impact of adjacent domains, a ratio of the quotient image and its modulus mean value is obtained. The exponent result of the ratio is closely approximate to a relative reflection component. Since the gray value of facial organs is less than that of the facial skin, postprocessing is applied to the images in order to highlight facial texture for face recognition. Experiments show that the performance by using the proposed method is superior to that of state of the arts. 1. Introduction Face recognition is one of the most active research focuses due to its wide applications [1, 2]. Face recognition can be used in many areas including security access control, surveillance monitor, and intelligent human machine interface. The accuracy of face recognition is not ideal at present. Among numerous adverse factors for face recognition, appearance variation caused by illumination is one of the major problems which remain unsettled. Many approaches such as illumination cones method [3] and 9D linear subspace method [4] have been proposed to solve illumination problems and improve the face recognition. The main drawbacks of the approaches mentioned above are the need of knowledge about the light source or a large volume of training data. To overcome this demerit, region-based image preprocessing methods are proposed in [5–7]. These methods introduced some noise to make global illumination discontinuous. Some illumination normalization methods are proposed to deal with the problem of varied lighting, which does not require training images with low computational complexity [8, 9], such as multiscale retinex (MSR) [10], wavelet-based normalization technique (WA) [11], and DCT-based normalization technique (DCT) [12]. The extracted facial feature is poor and has messy histogram. In order to highlight facial texture features, some methods are proposed including adaptive nonlocal means (ANL) [13], DoG filtering (DOG) [14], steerable filter (SF) [15], and wavelet denoising (WD) [16]. Overall gray values after processing are different with varying degrees and partial facial feature
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