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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Eigenfunctions Links Spectral Embedding and Kernel PCA
Neural Computation
Random Sampling for Subspace Face Recognition
International Journal of Computer Vision
Selecting Principal Components in a Two-Stage LDA Algorithm
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Journal of Cognitive Neuroscience
Orthogonal neighborhood preserving discriminant analysis for face recognition
Pattern Recognition
Feature Extraction and Uncorrelated Discriminant Analysis for High-Dimensional Data
IEEE Transactions on Knowledge and Data Engineering
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Locally Supervised Discriminant Analysis in Kernel Space
CIS '09 Proceedings of the 2009 International Conference on Computational Intelligence and Security - Volume 01
Face Recognition by Regularized Discriminant Analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Generalizing discriminant analysis using the generalized singular value decomposition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In the past few decades, many face recognition methods have been developed. Among these methods, subspace analysis is an effective approach for face recognition. Unsupervised discriminant projection (UDP) finds an embedding subspace that preserves local structure information, and uncovers and separates embedding corresponding to different manifolds. Though UDP has been applied in many fields, it has limits to solve the classification tasks, such as the ignorance of the class information. Thus, a novel subspace method, called supervised discriminant projection (SDP), is proposed for face recognition in this paper. In our method, the class information was utilized in the procedure of feature extraction. In SDP, the local structure of the original data is constructed according to a certain kind of similarity between data points, which takes special consideration of both the local information and class information. We test the performance of the proposed method SDP on three popular face image databases (i.e. AR database, Yale database, and a subset of FERET database). Experimental results show that the proposed method is effective.