Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Multilinear Singular Value Decomposition
SIAM Journal on Matrix Analysis and Applications
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
SIAM Journal on Matrix Analysis and Applications
Feature extraction approaches based on matrix pattern: MatPCA and MatFLDA
Pattern Recognition Letters
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Beyond streams and graphs: dynamic tensor analysis
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Algorithm 862: MATLAB tensor classes for fast algorithm prototyping
ACM Transactions on Mathematical Software (TOMS)
Knowledge and Information Systems
Application of the Karhunen-Loève Expansion to Feature Selection and Ordering
IEEE Transactions on Computers
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Joint manifold distance: a new approach to appearance based clustering
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
The Kernel Common Vector Method: A Novel Nonlinear Subspace Classifier for Pattern Recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Multilinear Discriminant Analysis for Face Recognition
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition
International Journal of Computer Vision and Image Processing
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The subspace classifiers are pattern classification methods where linear subspaces are used to represent classes. In order to use the classical subspace classifiers for face recognition tasks, two-dimensional (2D) image matrices must be transformed into one-dimensional (1D) vectors. In this paper, we propose new methods to apply the conventional subspace classifier methods directly to the image matrices. The proposed methods yield easier evaluation of correlation and covariance matrices, which in turn speeds up the training and testing phases of the classification process. Utilizing 2D image matrices also enables us to apply 2D versions of some subspace classifiers to the face recognition tasks, in which the corresponding classical subspace classifiers cannot be used due to high dimensionality. Moreover, the proposed methods are also generalized such that they can be used with the higher order image tensors. We tested the proposed 2D methods on three different face databases. Experimental results show that the performances of the proposed 2D methods are typically better than the performances of classical subspace classifiers in terms of recognition accuracy and real-time efficiency.