Using Discriminant Eigenfeatures for Image Retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
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
Knowledge and Information Systems
General Tensor Discriminant Analysis and Gabor Features for Gait Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A convergent solution to tensor subspace learning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
PCA based immune networks for human face recognition
Applied Soft Computing
An improved hybrid approach to face recognition by fusing local and global discriminant features
International Journal of Biometrics
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In this paper, we propose a novel method for image feature extraction. This method combines the ideas of two-dimensional principal component analysis and two-dimensional maximum scatter difference and which can directly extracts the optimal projective vectors from 2D image matrices rather than image vectors based on the scatter difference criterion. The proposed method not only avoids the singularity problem frequently occurred in the classical Fisher discriminant analysis due to the small sample size, but also saves much computational time. In addition, the proposed method can simultaneously make use of the discriminant information and descriptive information of the image. Experiments conducted on FERET, and ORL face databases demonstrate the effectiveness of the proposed method.