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
Graph Embedding: A General Framework for Dimensionality Reduction
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Two-dimensional Laplacianfaces method for face recognition
Pattern Recognition
2D-LDA: A statistical linear discriminant analysis for image matrix
Pattern Recognition Letters
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
Supervised Discriminant Projection with Its Application to Face Recognition
Neural Processing Letters
Image security and biometrics: a review
HAIS'12 Proceedings of the 7th international conference on Hybrid Artificial Intelligent Systems - Volume Part II
Multi-scale gist feature manifold for building recognition
Neurocomputing
Hi-index | 0.01 |
This paper proposes a novel method, called two-dimensional local graph embedding discriminant analysis (2DLGEDA), for image feature extraction, which can directly extract the optimal projective vectors from two-dimensional image matrices rather than image vectors based on the scatter difference criterion. In graph embedding, the intrinsic graph characterizes the intraclass compactness and connects each data point with its neighboring within the same class, while the penalty graph connects the marginal points and characterizes the interclass separability. The proposed method effectively avoids the singularity problem frequently encountered in the traditional linear discriminant analysis algorithm (LDA) due to the small sample size (SSS) and overcomes the limitations of LDA due to data distribution assumptions and available projection directions. Experimental results on ORL, YALE, FERET face databases and PolyU palmprint database show the effectiveness of the proposed method.