In this paper, we propose a novel low-rank regularized generic representation method to address the single sample per person (SSPP) 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. 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 for face sketch recognition.

In this paper, we propose a multistage KNN collaborative coding based Bag-of-Feature (MKCC-BoF) method to address SSPP problem, which tries to weaken the semantic gap between facial features and facial identification. First, local descriptors are extracted from the single training face images and a visual dictionary is obtained offline by clustering a large set of descriptors with K-means. Then, we design a multistage KNN collaborative coding scheme to project local features into the semantic space, which is much more efficient than the most commonly used non-negative sparse coding algorithm in face recognition. To describe the spatial information as well as reduce the feature dimension, the encoded features are then pooled on spatial pyramid cells by max-pooling, which generates a histogram of visual words to represent a face image. Finally, a SVM classifier based on linear kernel is trained with the concatenated features from pooling results. Experimental results on three public face databases show that the proposed MKCC-BoF is much superior to those specially designed methods for SSPP problem. Moreover, it also has great robustness to expression, illumination, occlusion and, time variation.

In this paper, we propose a uniform generic representation (UGR) method to solve the single sample per person (SSPP) problem in face recognition, which aims to find consistency between the global and local generic representations. For the local generic representation, we require the probe patches of the same image to be constructed respectively by the corresponding patches of the same gallery image and the intra-class variation dictionaries. Therefore, the probe patches’ coefficients, corresponding to patch gallery dictionaries, should be similar to each other. For the global generic representation, the probe image’s coefficient, corresponding to the gallery dictionary, should be similar to those of its probe patches. In order to meet the two requirements, we combine local generic representation with global generic representation in soft form. We obtain the representation coefficients by solving a simple quadratic optimization problem. UGR has been evaluated on Extended Yale B, AR, CMU-PIE, and LFW databases. Experimental results show the robustness and effectiveness of our method to illumination, expression, occlusion, time variation, and pose.