Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
The CMU Pose, Illumination, and Expression Database
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
Neighborhood Preserving Embedding
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Dimensionality Reduction of Multimodal Labeled Data by Local Fisher Discriminant Analysis
The Journal of Machine Learning Research
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Regularized locality preserving projections and its extensions for face recognition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
Face recognition using Intrinsicfaces
Pattern Recognition
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
Weighted discriminative sparsity preserving embedding for face recognition
Knowledge-Based Systems
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Though principle component analysis (PCA) and locality preserving projections (LPPs) are two of the most popular linear methods for face recognition, PCA can only see the Euclidean structure of the training set and LPP preserves the nonlinear submanifold structure hidden in the training set. In this paper, we propose the elastic preserving projections (EPPs) which by incorporating the merits of the local geometry and the global information of the training set. EPP outputs a sample subspace which simultaneously preserves the local geometrical structure and exploits the global information of the training set. Different from some other linear dimensionality reduction methods, EPP can be deemed as learning both the coordinates and the affinities between sample points. Furthermore, the effectiveness of our proposed algorithm is analyzed theoretically and confirmed by some experiments on several well-known face databases. The obtained results indicate that EPP significantly outperforms its other rival algorithms.