Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
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
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Feature extraction based on Laplacian bidirectional maximum margin criterion
Pattern Recognition
Letters: Feature extraction using fuzzy inverse FDA
Neurocomputing
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A multi-manifold discriminant analysis method for image feature extraction
Pattern Recognition
Face Recognition Using Kernel UDP
Neural Processing Letters
Face recognition using LDA-based algorithms
IEEE Transactions on Neural Networks
| Hi-index | 0.01 |
In this paper, we present a new nonlinear feature extraction method for face recognition. The proposed method incorporates the kernel trick with inverse Fisher discriminant analysis and develops a two-phase kernel inverse Fisher discriminant analysis criterion - KPCA plus IFDA. In the proposed method, we first apply the nonlinear kernel trick to map the original face samples into an implicit feature space and then perform inverse Fisher discriminant analysis in the feature space to produce nonlinear discriminating features. In implementation, kernel IFDA seeks nonlinear discriminating features by minimizing the inverse Fisher discriminant quotient and overcome the singularity problem by projective transformation of scatter matrices. Experimental results on ORL, FERET and AR face databases demonstrate the effectiveness of the proposed method.