Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
Neural Networks - 2005 Special issue: IJCNN 2005
Is two-dimensional PCA equivalent to a special case of modular PCA?
Pattern Recognition Letters
Journal of Cognitive Neuroscience
Principal Component Analysis Based on L1-Norm Maximization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast image compression based on (2D)2 PCA
CSNA '07 Proceedings of the IASTED International Conference on Communication Systems, Networks, and Applications
Hierarchical ensemble of global and local classifiers for face recognition
IEEE Transactions on Image Processing
Wood Classification Based on PCA, 2DPCA, (2D)2PCA and LDA
KAM '09 Proceedings of the 2009 Second International Symposium on Knowledge Acquisition and Modeling - Volume 01
Block-wise 2D kernel PCA/LDA for face recognition
Information Processing Letters
Directional binary code with application to PolyU near-infrared face database
Pattern Recognition Letters
Letters: Laplacian bidirectional PCA for face recognition
Neurocomputing
Representing image matrices: eigenimages versus eigenvectors
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Block principal component analysis with L1-norm for image analysis
Pattern Recognition Letters
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Two-Dimensional Principal Component Analysis (2DPCA) is a well-known feature extraction method for face recognition. One of the main drawbacks of this method, in comparison with the vector-based PCA, is that it needs many more coefficients to represent the feature matrix of an image. Two-Directional 2DPCA ((2D)^2PCA), proposed in the literature, attempts to alleviate this problem. However, it fails to improve the recognition accuracy of 2DPCA. In addition, (2D)^2PCA follows a global feature extraction approach that might fail to preserve some important local features. In this paper, we propose Block-Wise (2D)^2PCA to enhance the performance of (2D)^2PCA by preserving the local informative variations. On average, the feature matrices produced by the proposed method and those formed by (2D)^2PCA are about the same size. However, our experiments on four face recognition databases indicate that our method is superior to (2D)^2PCA in terms of the recognition accuracy.