Total variation models based algorithm of illumination normalization for face recognition

This paper proposes a new method for the face illumination normalization, combining Total variation models with Contourlet transform. We iterate the high-frequency information decomposed by the Contourlet transform. Then extract the face illumination normalization by Contourlet Inverse transform. This algorithm takes full advantage of the multidimensional 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 and the results demonstrate that the proposed method achieves satisfactory recognition rates under varying illumination conditions. Compared with the Contourlet transform method and the traditional total variation models, the proposed method has an average recognition ratio increase 9.6% and 2.55%.