Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29513
Title: A novel subspace-based facial discriminant feature extraction method
Authors: Song, F
Xu, Y
Zhang, D 
Liu, T
Keywords: Face recognition
Feature extraction
Linear discriminant analysis
Orthogonal procedure
Issue Date: 2009
Source: Proceedings of the 2009 Chinese Conference on Pattern Recognition, CCPR 2009, and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR, 2009, p. 869-873 How to cite?
Abstract: This paper presented a novel subspace-based facial discriminant feature extraction method, i.e. Orthogonalized Direct Linear Discriminant Analysis (OD-LDA), whose discriminant vectors could be obtained by performing Gram-Schmidt orthogonal procedure on a set of discriminant vectors of D-LDA. Experimental studies conducted on ORL, FERET, Yale, and AR face image databases showed that OD-LDA could compete with prevailing subspace-based facial discriminant feature extraction methods such as Fisherfaces, N-LDA D-LDA, Uncorrelated LDA, Parameterized D-LDA, K-L expansion based the between-class scatter matrix, and Orthogonal Complimentary Space Method in terms of recognition rate.
Description: 2009 Chinese Conference on Pattern Recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern Recognition, CJKPR, Nanjing, 4-6 November 2009
URI: http://hdl.handle.net/10397/29513
ISBN: 9781424441990
DOI: 10.1109/CCPR.2009.5343963
Appears in Collections:Conference Paper

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