Application of the Karhunen-Loeve Procedure for the Characterization of Human Faces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Illumination insensitive recognition using eigenspaces
Computer Vision and Image Understanding
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
Factored principal components analysis, with applications to face recognition
Statistics and Computing
Fusion of support vector classifiers for parallel gabor methods applied to face verification
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Real-time subspace-based background modeling using multi-channel data
ISVC'07 Proceedings of the 3rd international conference on Advances in visual computing - Volume Part II
Random subspace two-dimensional PCA for face recognition
PCM'07 Proceedings of the multimedia 8th Pacific Rim conference on Advances in multimedia information processing
Orthogonal linear discriminant analysis and feature selection for micro-array data classification
Expert Systems with Applications: An International Journal
Block-wise 2D kernel PCA/LDA for face recognition
Information Processing Letters
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Neighborhood dependent approximation by nonlinear embedding for face recognition
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Palmprint verification using GridPCA for Gabor features
Proceedings of the Second Symposium on Information and Communication Technology
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part I
Kernel principal component analysis for large scale data set
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Block principal component analysis with L1-norm for image analysis
Pattern Recognition Letters
Two-Dimensional optimal transform for appearance based object recognition
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Journal of Biomedical Imaging - Special issue on Machine Learning in Medical Imaging
Survey: Subspace methods for face recognition
Computer Science Review
Three-fold structured classifier design based on matrix pattern
Pattern Recognition
New color GPHOG descriptors for object and scene image classification
Machine Vision and Applications
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In the tasks of image representation, recognition and retrieval, a 2D image is usually transformed into a 1D long vector and modelled as a point in a high-dimensional vector space. This vector-space model brings up much convenience and many advantages. However, it also leads to some problems such as the Curse of Dimensionality dilemma and Small Sample Size problem, and thus produces us a series of challenges, for example, how to deal with the problem of numerical instability in image recognition, how to improve the accuracy and meantime to lower down the computational complexity and storage requirement in image retrieval, and how to enhance the image quality and meanwhile to reduce the transmission time in image transmission, etc. In this paper, these problems are solved, to some extent, by the proposed Generalized 2D Principal Component Analysis (G2DPCA). G2DPCA overcomes the limitations of the recently proposed 2DPCA (Yang et al., 2004) from the following aspects: (1) the essence of 2DPCA is clarified and the theoretical proof why 2DPCA is better than Principal Component Analysis (PCA) is given; (2) 2DPCA often needs much more coefficients than PCA in representing an image. In this work, a Bilateral-projection-based 2DPCA (B2DPCA) is proposed to remedy this drawback; (3) a Kernel-based 2DPCA (K2DPCA) scheme is developed and the relationship between K2DPCA and KPCA (Scholkopf et al., 1998) is explored. Experimental results in face image representation and recognition show the excellent performance of G2DPCA.