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
From Few to Many: Illumination Cone Models for Face Recognition under Variable Lighting and Pose
IEEE Transactions on Pattern Analysis and Machine Intelligence
Empirical Performance Analysis of Linear Discriminant Classifiers
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Local Discriminant Embedding and Its Variants
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The Journal of Machine Learning Research
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
Journal of Cognitive Neuroscience
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Discriminant Analysis for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph-optimized locality preserving projections
Pattern Recognition
Locality preserving fisher discriminant analysis for face recognition
ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications
Maximum Correntropy Criterion for Robust Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Supervised optimal locality preserving projection
Pattern Recognition
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Efficient and robust feature extraction by maximum margin criterion
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
In this paper, a new linear dimension reduction method called supervised orthogonal discriminant subspace projection (SODSP) is proposed, which addresses high-dimensionality of data and the small sample size problem. More specifically, given a set of data points in the ambient space, a novel weight matrix that describes the relationship between the data points is first built. And in order to model the manifold structure, the class information is incorporated into the weight matrix. Based on the novel weight matrix, the local scatter matrix as well as non-local scatter matrix is defined such that the neighborhood structure can be preserved. In order to enhance the recognition ability, we impose an orthogonal constraint into a graph-based maximum margin analysis, seeking to find a projection that maximizes the difference, rather than the ratio between the non-local scatter and the local scatter. In this way, SODSP naturally avoids the singularity problem. Further, we develop an efficient and stable algorithm for implementing SODSP, especially, on high-dimensional data set. Moreover, the theoretical analysis shows that LPP is a special instance of SODSP by imposing some constraints. Experiments on the ORL, Yale, Extended Yale face database B and FERET face database are performed to test and evaluate the proposed algorithm. The results demonstrate the effectiveness of SODSP.