Letters: Two-dimensional direct and weighted linear discriminant analysis for face recognition

Authors:
Ruicong Zhi;Qiuqi Ruan
Affiliations:
Institute of Information Science, Beijing Jiaotong University, Beijing 100044, PR China;Institute of Information Science, Beijing Jiaotong University, Beijing 100044, PR China
Venue:
Neurocomputing
Year:
2008

Citing 4
Cited 4

Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiclass Linear Dimension Reduction by Weighted Pairwise Fisher Criteria

IEEE Transactions on Pattern Analysis and Machine Intelligence
2D Direct LDA Algorithm for Face Recognition

SERA '06 Proceedings of the Fourth International Conference on Software Engineering Research, Management and Applications
2D-LDA: A statistical linear discriminant analysis for image matrix

Pattern Recognition Letters

Face Recognition Based on Histogram of Modular Gabor Feature and Support Vector Machines

ISNN 2009 Proceedings of the 6th International Symposium on Neural Networks: Advances in Neural Networks - Part III
Face recognition by generalized two-dimensional FLD method and multi-class support vector machines

Applied Soft Computing
An improved hybrid approach to face recognition by fusing local and global discriminant features

International Journal of Biometrics
Discriminative Zernike and Pseudo Zernike Moments for Face Recognition

International Journal of Computer Vision and Image Processing

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this paper, a novel algorithm for feature extraction-two-dimensional direct and weighted linear discriminant analysis (2D-DWLDA)-is proposed. The improvement of 2D-DWLDA algorithm over traditional linear discriminant analysis (LDA) and 2D-LDA methods benefits mostly from three aspects: (1) 2D-DWLDA is based on 2D image matrices rather than 1D vectors, so the scatter matrices can be constructed directly using the image matrices, and calculated accurately; (2) by introducing weighting function, the overlap of the neighboring classes is weaken; (3) direct LDA method is utilized so that the extracted features have more discriminant power. Finally, we performed a series of experiments on three face databases: ORL, CAS-PEAL and Yale database, the recognition accuracies are higher using 2D-DWLDA than 2D-LDA and LDA.