Matrix analysis
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis with Tensor Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Generalized Low Rank Approximations of Matrices
Machine Learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face recognition based on a novel linear discriminant criterion
Pattern Analysis & Applications
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Sparsity preserving projections with applications to face recognition
Pattern Recognition
Enhancing Bilinear Subspace Learning by Element Rearrangement
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-dimensional discriminant locality preserving projections for face recognition
Pattern Recognition Letters
Face recognition using discriminant locality preserving projections
Image and Vision Computing
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Fast communication: Active energy image plus 2DLPP for gait recognition
Signal Processing
Locality preserving discriminant projections
ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications
A two-step framework for highly nonlinear data unfolding
Neurocomputing
Locality preserving fisher discriminant analysis for face recognition
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Generalized low-rank approximations of matrices revisited
IEEE Transactions on Neural Networks
Efficient face recognition using tensor subspace regression
Neurocomputing
Tensor distance based multilinear locality-preserved maximum information embedding
IEEE Transactions on Neural Networks
IEEE Transactions on Information Forensics and Security
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Discriminant information (DI) plays a critical role in face recognition. In this paper, we proposed a second-order discriminant tensor subspace analysis (DTSA) algorithm to extract discriminant features from the intrinsic manifold structure of the tensor data. DTSA combines the advantages of previous methods with DI, the tensor methods preserving the spatial structure information of the original image matrices, and the manifold methods preserving the local structure of the samples distribution. DTSA defines two similarity matrices, namely within-class similarity matrix and between-class similarity matrix. The within-class similarity matrix is determined by the distances of point pairs in the same class, while the between-class similarity matrix is determined by the distances between the means of each pair of classes. Using these two matrices, the proposed method preserves the local structure of the samples to fit the manifold structure of facial images in high dimensional space better than other methods. Moreover, compared to the 2D methods, the tensor based method employs two-sided transformations rather than single-sided one, and yields higher compression ratio. As a tensor method, DTSA uses an iterative procedure to calculate the optimal solution of two transformation matrices. In this paper, we analyzed DTSA's connections to 2D-DLPP and TSA, theoretically. The experiments on the ORL, Yale and YaleB facial databases show the effectiveness of the proposed method.