| Face Recognition Technology has been widely used in practice, but most face recognition algorithms are very sensitive to light, illumination has became one of the most decisive factors for the recognition result. One of most common method for illumination problems is looking for a illumination normalization, the illumination normalization mainly include high-frequency and edge information of image, it is insensitive to light and can describe image under different light conditions. TV model is an effective method for extracting high-frequency and edge information of image.TV model can well maintain the edge of image and extract most useful high-frequency details for face recognition, but the illumination normalization obtained by TV model based L norm is not precise, and the parameters of this TV model is very random. To solve the above problems, we lead to the TV model based on G-norm, and propose an adaptive G-norm TV model, this model can obtain more accurate illumination normalization, and it is more conductive to recognize. To solve the globalization and the constant area of TV model, we propose a new model combing TV model and Contourlet transform, This algorithm takes full advantage of localization and the multi-dimensional of Contourlet transform and the edge-preserve ability of total variation models, it can effectively obtain the face illumination normalization for the face recognition.Experiments are carried out Upon the Yale-B database demonstrate that the proposed method achieves satisfactory recognition rates under varying illumination conditions. Compared with directly method of PCA+LDA, the proposed method has an average recognition ratio increase 40.11% and 40.65%. and all increase 86.87% in the worst light conditions; Compared with the traditional TV model, has an average recognition ratio increase 1.91% and 2.45%, and respectively increase 1.41% and 1.95%, so the proposed model are effective method for face illumination normalization. |
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