Learning Low-Rank Regularized Generic Representation With Block-Sparse Structure for Single Sample Face Recognition

In this paper, we propose a novel low-rank regularized generic representation method to address the single sample per person problem in face recognition, which simultaneously employs the structure information from the probe dataset and the generic variation information. Each face image is divided into overlapped patches, whose classification results are aggregated to produce the final result. We generate a subspace for each patch by extracting its eight nearest neighbor patches and explore the relationships between subspaces by imposing a low-rank constraint on the reconstruction coefficients. Moreover, a block-sparsity constraint on the coefficient matrix is imposed to further promote the discrimination of representations. We also propose a dictionary learning method to learn the intra-class facial variations from the generic face datasets, which separates the face contour noise from the variation dictionary by an incoherence regularization item. The experimental results on four public face databases not only show the robustness of our approach to expression, illumination, occlusion, and time variation but also demonstrate the effectiveness of face sketch recognition.