Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
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
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
(2D)2LDA: An efficient approach for face recognition
Pattern Recognition
Journal of Cognitive Neuroscience
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
On approaching 2D-FPCA technique to improve image representation in frequency domain
Proceedings of the Fourth Symposium on Information and Communication Technology
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Although 2D-PCA and 2D-LDA algorithms obtain high recognition accuracy, drawback of these is that they need huge feature matrices for the task of face recognition. Besides, structure information between row and column direction cannot be uncovered simultaneously. To overcome these problems, this paper presents an efficient approach for face image feature extraction - a novel two-stage discrimination approach: preprocess original images to get two new image matrices and represent these images matrices using bidirectional 2D-LDA techniques. This approach directly extracts the optimal projective vectors from two new 2D image matrices by simultaneously considering row-direction 2D-LDA and column direction 2D-LDA. With this proposal, we can utilize the idea of local block features and global 2D images structures so it can preserve the 2D local facial features. Experimental results on ORL and Yale face database demonstrate that the proposed method obtains good recognition accuracy despite having less number of coefficient and few training samples (about two samples for each class).