Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Acquiring Linear Subspaces for Face Recognition under Variable Lighting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized spectral bounds for sparse LDA
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Learning with l1-graph for image analysis
IEEE Transactions on Image Processing
Kernel sparse representation for image classification and face recognition
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part IV
Beyond sparsity: The role of L1-optimizer in pattern classification
Pattern Recognition
Discriminant sparse neighborhood preserving embedding for face recognition
Pattern Recognition
Face recognition using Elasticfaces
Pattern Recognition
IEEE Transactions on Information Theory
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
Tensor Discriminant Color Space for Face Recognition
IEEE Transactions on Image Processing
Face recognition via Weighted Sparse Representation
Journal of Visual Communication and Image Representation
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Sparse representation (SR) based dimension reduction (DR) methods have aroused lots of interests in the field of face recognition. In this paper, we firstly propose a new sparse representation method called weighted elastic net (WEN). Compared to the existing SR methods, WEN is able to explore and use the local structures of data sets sufficiently. Based on WEN, a new supervised sparse representation based DR algorithm called weighted discriminative sparsity preserving embedding (WDSPE) is proposed. In WDSPE, the within-class scatter and between-class scatter of a given data set are constructed by using WEN. Consequently, WDSPE seeks a subspace in which the ratio of the between-class scatter to the within-class scatter is maximized. Moreover, by integrating the global discriminative structures of data sets, we present an extension version of WDSPE. Experiments conducted on three popular face databases (Yale, AR and the extended Yale B) with promising results demonstrate the feasibility and effectiveness of the proposed methods.