The nature of statistical learning theory
The nature of statistical learning theory
Manifold Pursuit: A New Approach to Appearance Based Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Orthogonal Laplacianfaces for Face Recognition
IEEE Transactions on Image Processing
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
A new implementation of common matrix approach using third-order tensors for face recognition
Expert Systems with Applications: An International Journal
Global plus local: A complete framework for feature extraction and recognition
Pattern Recognition
Hi-index | 0.01 |
Face image data taken with various capturing devices are usually high dimensional and not very suitable for accurate classification. Recently, a lot of manifold learning algorithms have been used in face recognition community. Among them, locality preserving projections (LPP) is one of the most promising feature extraction techniques. In this paper, a new face recognition method based on orthogonal discriminant locality preserving projections (ODLPP) is proposed. Based on LPP, ODLPP takes into account the between-class information, changes the objective function, and then orthogonalizes the basis vectors of the face subspace. The proposed method was compared with eigenface, Fisherface, orthogonal LPP (OLPP) and Laplacianface methods on the Yale and AR face databases. Experimental results indicated the promising performance of the proposed method.