Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Face Recognition Using Laplacianfaces
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Face recognition based on 2D Fisherface approach
Pattern Recognition
Journal of Cognitive Neuroscience
Locally linear discriminant embedding: An efficient method for face recognition
Pattern Recognition
Face recognition using discriminant locality preserving projections
Image and Vision Computing
Rapid and brief communication: Two-dimensional FLD for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
Two dimensional laplacianfaces method for face recognition
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
Plant classification using leaf image based on 2D linear discriminant analysis
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
The analysis of parameters t and k of LPP on several famous face databases
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
Hi-index | 0.10 |
One of the key issues of face recognition is to extract the features of face images. 2D-DLPP is a new feature extraction method for face recognition. 2D-DLPP benefits from three techniques, i.e., locality preserving projections (LPP), image based projection and discriminant analysis. Firstly, LPP can optimally preserve the local structure of the samples. Secondly, compared to vector based projection, image based projection can avoid the small sample size problem and give more spatial structural information of image. Finally, discriminant analysis applied in 2D-DLPP can improve recognition performance by maximizing the interpersonal distance and minimizing the intrapersonal distance. The experimental results show that 2D-DLPP is robust and has better face recognition performance than other methods. In addition, row projection and column projection of 2D-DLPP are discussed and compared. Though the expressions of the two projections are similar, the experimental results of them are different. Therefore, it is necessary to select a suitable projection method for a certain face image database.