Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Enhanced Fisher Linear Discriminant Models for Face Recognition
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
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
Local Fisher discriminant analysis for supervised dimensionality reduction
ICML '06 Proceedings of the 23rd international conference on Machine learning
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Locality sensitive discriminant analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
Two-dimensional supervised local similarity and diversity projection
Pattern Recognition
Joint geometry and variability for image recognition
Neurocomputing
Feature extraction using two-dimensional neighborhood margin and variation embedding
Computer Vision and Image Understanding
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Previous works have demonstrated that manifold-based learning discriminant approaches can improve the face recognition accuracy. However, they ignore the variation among nearby face images from the same class, which is important to further improve the recognition accuracy and avoid the over-fitting problem in discriminant approaches. To avoid this problem, we propose a novel approach for face recognition. In our proposed approach, we construct two adjacency graphs to model the margin and information including similarity and variation of face images from the same class, respectively, and then incorporate the information and margin into the dimensionality reduction function. Experiments demonstrate the effectiveness of our approach.