Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Coding Facial Expressions with Gabor Wavelets
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Comprehensive Database for Facial Expression Analysis
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Two-dimensional discriminant locality preserving projections for face recognition
Pattern Recognition Letters
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Two-dimensional supervised local similarity and diversity projection
Pattern Recognition
Facial affect recognition using regularized discriminant analysis-based algorithms
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
LPP solution schemes for use with face recognition
Pattern Recognition
Impact of implicit and explicit affective labeling on a recommender system's performance
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Fusion of feature sets and classifiers for facial expression recognition
Expert Systems with Applications: An International Journal
A neural-AdaBoost based facial expression recognition system
Expert Systems with Applications: An International Journal
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In this paper, a novel method called two-dimensional discriminant locality preserving projections (2D-DLPP) is proposed. By introducing between-class scatter constraint and label information into two-dimensional locality preserving projections (2D-LPP) algorithm, 2D-DLPP successfully finds the subspace which can best discriminate different pattern classes. So the subspace obtained by 2D-DLPP has more discriminant power than 2D-LPP, and is more suitable for recognition tasks. The proposed method was applied to facial expression recognition tasks on JAFFE and Cohn-Kanade database and compared with other three widely used two-dimensional methods: 2D-PCA, 2D-LDA and 2D-LPP. The high recognition rates show the effectiveness of the proposed algorithm.