科研之友
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摘要
Recently Sparse Representation (or coding) based Classification (SRC) has gained great success in face recognition. In SRC, the testing image is expected to be best represented as a sparse linear combination of training images from the same class, and the representation fidelity is measured by the a"" (2)-norm or a"" (1)-norm of the coding residual. However, SRC emphasizes the sparsity too much and overlooks the spatial information during local feature encoding process which has been demonstrated to be critical in real-world face recognition problems. Besides, some work considers the spatial information but overlooks the different discriminative ability in different face regions. In this paper, we propose to weight spatial locations based on their discriminative abilities in sparse coding for robust face recognition. Specifically, we learn the weights at face locations according to the information entropy in each face region, so as to highlight locations in face images that are important for classification. Furthermore, in order to construct a robust weights to fully exploit structure information of each face region, we employed external data to learn the weights, which can cover all possible face image variants of different persons, so the robustness of obtained weights can be guaranteed. Finally, we consider the group structure of training images (i.e. those from the same subject) and added an a"" (2,1)-norm (group Lasso) constraint upon the formulation, which enforcing the sparsity at the group level. Extensive experiments on three benchmark face datasets demonstrate that our proposed method is much more robust and effective than baseline methods in dealing with face occlusion, corruption, lighting and expression changes, etc.
