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
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fractional-Step Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria
IEEE Transactions on Pattern Analysis and Machine Intelligence
Principal Manifolds and Probabilistic Subspaces for Visual Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Kernel Eigenfaces vs. Kernel Fisherfaces: Face Recognition Using Kernel Methods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Face Recognition Using Kernel Based Fisher Discriminant Analysis
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gabor-Based Kernel PCA with Fractional Power Polynomial Models for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Kernel Fractional-Step Nonlinear Discriminant Analysis for Pattern Recognition
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Two-Stage Linear Discriminant Analysis via QR-Decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fisher+Kernel Criterion for Discriminant Analysis
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Journal of Cognitive Neuroscience
Rapid and brief communication: An efficient kernel discriminant analysis method
Pattern Recognition
Kernel machine-based one-parameter regularized Fisher discriminant method for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An optimization criterion for generalized discriminant analysis on undersampled problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust coding schemes for indexing and retrieval from large face databases
IEEE Transactions on Image Processing
Improving kernel Fisher discriminant analysis for face recognition
IEEE Transactions on Circuits and Systems for Video Technology
Face recognition using kernel direct discriminant analysis algorithms
IEEE Transactions on Neural Networks
A two-stage head pose estimation framework and evaluation
Pattern Recognition
Kernel based subspace methods: infrared vs visible face
Machine Graphics & Vision International Journal
Kernel discriminant transformation for image set-based face recognition
Pattern Recognition
A new discriminant analysis approach under decision-theoretic rough sets
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Weighted generalized kernel discriminant analysis using fuzzy memberships
WSEAS Transactions on Mathematics
WSEAS Transactions on Mathematics
Hi-index | 0.01 |
Feature extraction is among the most important problems in face recognition systems. In this paper, we propose an enhanced kernel discriminant analysis (KDA) algorithm called kernel fractional-step discriminant analysis (KFDA) for nonlinear feature extraction and dimensionality reduction. Not only can this new algorithm, like other kernel methods, deal with nonlinearity required for many face recognition tasks, it can also outperform traditional KDA algorithms in resisting the adverse effects due to outlier classes. Moreover, to further strengthen the overall performance of KDA algorithms for face recognition, we propose two new kernel functions: cosine fractional-power polynomial kernel and non-normal Gaussian RBF kernel. We perform extensive comparative studies based on the YaleB and FERET face databases. Experimental results show that our KFDA algorithm outperforms traditional kernel principal component analysis (KPCA) and KDA algorithms. Moreover, further improvement can be obtained when the two new kernel functions are used.