Learning from Real Images to Model Lighting Variations for Face Images

For robust face recognition, the problem of lighting variation is considered as one of the greatest challenges. Since the nine points of light (9PL) subspace is an appropriate low-dimensional approximation to the illumination cone, it yielded good face recognition results under a wide range of difficult lighting conditions. However building the 9PL subspace for a subject requires 9 gallery images under specific lighting conditions, which are not always possible in practice. Instead, we propose a statistical model for performing face recognition under variable illumination. Through this model, the nine basis images of a face can be recovered via maximum-a-posteriori (MAP) estimation with only one gallery image of that face. Furthermore, the training procedure requires only some real images and avoids tedious processing like SVD decomposition or the use of geometric (3D) or albedo information of a surface. With the recovered nine dimensional lighting subspace, recognition experiments were performed extensively on three publicly available databases which include images under single and multiple distant point light sources. Our approach yields better results than current ones. Even under extreme lighting conditions, the estimated subspace can still represent lighting variation well. The recovered subspace retains the main characteristics of 9PL subspace. Thus, the proposed algorithm can be applied to recognition under variable lighting conditions.