Incremental learning of bidirectional principal components for face recognition
Pattern Recognition
A novel kernel-based maximum a posteriori classification method
Neural Networks
Two-dimensional maximum margin feature extraction for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
Centroid neural network for face recognition
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Generalized discriminant analysis: a matrix exponential approach
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Individual identification using personality traits
Journal of Network and Computer Applications
Image ratio features for facial expression recognition application
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Regularized locality preserving projections and its extensions for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A Novel Regularization Learning for Single-View Patterns: Multi-View Discriminative Regularization
Neural Processing Letters
Robust classifiers for data reduced via random projections
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Supervised Discriminant Projection with Its Application to Face Recognition
Neural Processing Letters
Gradient-based local descriptor and centroid neural network for face recognition
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part II
Robust classification using l2,1-norm based regression model
Pattern Recognition
A feature selection method using improved regularized linear discriminant analysis
Machine Vision and Applications
| Hi-index | 0.00 |
When the feature dimension is larger than the number of samples the small sample-size problem occurs. There is great concern about it within the face recognition community. We point out that optimizing the Fisher index in linear discriminant analysis does not necessarily give the best performance for a face recognition system. We propose a new regularization scheme. The proposed method is evaluated using the Olivetti research laboratory database, the Yale database, and the Feret database.