DocumentCode :
1169300
Title :
GA-fisher: a new LDA-based face recognition algorithm with selection of principal components
Author :
Zheng, Wei-Shi ; Lai, Jian-Huang ; Yuen, Pong C.
Author_Institution :
Math. Dept., Sun Yat-sen Univ., Guangzhou, China
Volume :
35
Issue :
5
fYear :
2005
Firstpage :
1065
Lastpage :
1078
Abstract :
This paper addresses the dimension reduction problem in Fisherface for face recognition. When the number of training samples is less than the image dimension (total number of pixels), the within-class scatter matrix (Sw) in linear discriminant analysis (LDA) is singular, and principal component analysis (PCA) is suggested to employ in Fisherface for dimension reduction of Sw so that it becomes nonsingular. The popular method is to select the largest nonzero eigenvalues and the corresponding eigenvectors for LDA. To attenuate the illumination effect, some researchers suggested removing the three eigenvectors with the largest eigenvalues and the performance is improved. However, as far as we know, there is no systematic way to determine which eigenvalues should be used. Along this line, this paper proposes a theorem to interpret why PCA can be used in LDA and an automatic and systematic method to select the eigenvectors to be used in LDA using a genetic algorithm (GA). A GA-PCA is then developed. It is found that some small eigenvectors should also be used as part of the basis for dimension reduction. Using the GA-PCA to reduce the dimension, a GA-Fisher method is designed and developed. Compared with the traditional Fisherface method, the proposed GA-Fisher offers two additional advantages. First, optimal bases for dimensionality reduction are derived from GA-PCA. Second, the computational efficiency of LDA is improved by adding a whitening procedure after dimension reduction. The Face Recognition Technology (FERET) and Carnegie Mellon University Pose, Illumination, and Expression (CMU PIE) databases are used for evaluation. Experimental results show that almost 5% improvement compared with Fisherface can be obtained, and the results are encouraging.
Keywords :
eigenvalues and eigenfunctions; face recognition; genetic algorithms; matrix algebra; principal component analysis; GA-Fisher method; LDA-based face recognition algorithm; eigenvalues; eigenvector; genetic algorithm; image dimension reduction; linear discriminant analysis; principal component analysis; scatter matrix; Computational efficiency; Design methodology; Eigenvalues and eigenfunctions; Face recognition; Genetic algorithms; Lighting; Linear discriminant analysis; Pixel; Principal component analysis; Scattering; Dimension reduction; GA-PCA; LDA; PCA; face recognition; genetic algorithms; Algorithms; Artificial Intelligence; Computer Simulation; Discriminant Analysis; Face; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Linear Models; Models, Biological; Pattern Recognition, Automated; Principal Component Analysis; Subtraction Technique;
fLanguage :
English
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher :
ieee
ISSN :
1083-4419
Type :
jour
DOI :
10.1109/TSMCB.2005.850175
Filename :
1510780
Link To Document :
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