Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Unified Subspace Analysis for Face Recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Boosting Local Feature Based Classifiers for Face Recognition
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 5 - Volume 05
Subspace Analysis Using Random Mixture Models
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Random Subspaces and Subsampling for 2-D Face Recognition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Random sampling LDA for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Null space-based kernel fisher discriminant analysis for face recognition
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
IEEE Transactions on Image Processing
Rough set theory with discriminant analysis in analyzing electricity loads
Expert Systems with Applications: An International Journal
Semi-random subspace method for face recognition
Image and Vision Computing
A Random Network Ensemble for Face Recognition
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Semi-supervised classification based on random subspace dimensionality reduction
Pattern Recognition
Fast Kernel Discriminant Analysis for Classification of Liver Cancer Mass Spectra
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Semi-supervised ensemble classification in subspaces
Applied Soft Computing
Computers & Mathematics with Applications
Approximate polytope ensemble for one-class classification
Pattern Recognition
Detecting Facial Expressions for Monitoring Patterns of Emotional Behavior
International Journal of Monitoring and Surveillance Technologies Research
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Linear discriminant analysis (LDA) often suffers from the small sample size problem when dealing with high-dimensional face data. Random subspace can effectively solve this problem by random sampling on face features. However, it remains a problem how to construct an optimal random subspace for discriminant analysis and perform the most efficient discriminant analysis on the constructed random subspace. In this paper, we propose a novel framework, random discriminant analysis (RDA), to handle this problem. Under the most suitable situation of the principal subspace, the optimal reduced dimension of the face sample is discovered to construct a random subspace where all the discriminative information in the face space is distributed in the two principal subspaces of the within-class and between-class matrices. Then we apply Fisherface and direct LDA, respectively, to the two principal subspaces for simultaneous discriminant analysis. The two sets of discriminant analysis features from dual principal subspaces are first combined at the feature level, and then all the random subspaces are further integrated at the decision level. With the discriminating information fusion at the two levels, our method can take full advantage of useful discriminant information in the face space. Extensive experiments on different face databases demonstrate its performance.