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IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Visual learning and recognition of 3-D objects from appearance
International Journal of Computer Vision
Kernel PCA and de-noising in feature spaces
Proceedings of the 1998 conference on Advances in neural information processing systems II
Face recognition with one training image per person
Pattern Recognition Letters
Discriminant Analysis of Principal Components for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
An improved face recognition technique based on modular PCA approach
Pattern Recognition Letters
GPCA: an efficient dimension reduction scheme for image compression and retrieval
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
The equivalence of two-dimensional PCA to line-based PCA
Pattern Recognition Letters
Complete Two-Dimensional PCA for Face Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improving the interest operator for face recognition
Expert Systems with Applications: An International Journal
Letters: Laplacian bidirectional PCA for face recognition
Neurocomputing
Face recognition using Zernike and complex Zernike moment features
Pattern Recognition and Image Analysis
An improvement to matrix-based LDA
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Weighted principal component analysis
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
Block principal component analysis with L1-norm for image analysis
Pattern Recognition Letters
Computerized wrist pulse signal diagnosis using KPCA
ICMB'10 Proceedings of the Second international conference on Medical Biometrics
Two-phase test sample representation with efficient m-nearest neighbor selection in face recognition
ISNN'12 Proceedings of the 9th international conference on Advances in Neural Networks - Volume Part II
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition
International Journal of Computer Vision and Image Processing
Dimension reduction methods for image pattern recognition
SIMBAD'13 Proceedings of the Second international conference on Similarity-Based Pattern Recognition
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By formulating two-dimensional principle component analysis (2DPCA) as a mathematical form different from the conventional 2DPCA, we present theoretical basis of 2DPCA and show the theoretical similarities and differences between 2DPCA and PCA. We also show that 2DPCA owns its decorrelation property and the feature vectors extracted from matrices are uncorrelated. We use the proposed mathematical form to show that 2DPCA is the best approach for directly extract features from matrices. We also present in detail 2DPCA Schemes 1 and 2, two schemes to implement the proposed mathematical form. The two schemes transform original images into different spaces, respectively, 2DPCA Scheme 1 enhances the transverse characters of images, whereas the second scheme enhances vertical characters of images. We propose a feature fusion approach for achieving better recognition results by combining the features generated from the two schemes of 2DPCA. The proposed fusion approach is tested on face recognition tasks and is found to be more accurate than both 2DPCA Scheme 1 and 2DPCA Scheme 2.