Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15650
Title: Two-dimensional fisher discriminant analysis and its application to face recognition
Authors: Liang, Z
Shi, P
Zhang, D 
Issue Date: 2006
Publisher: Springer
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2006, v. 3851 LNCS, p. 130-139 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In order to effectively deal with this problem, a new technique for two-dimensional(2D) Fisher discriminant analysis is developed in this paper. In the proposed algorithm, the Fisher criterion function is directly constructed in terms of image matrices. Then we utilize the Fisher criterion and statistical correlation between features to construct an objective function. We theoretically analyze that the proposed algorithm is equivalent to uncorrelated two-dimensional discriminant analysis in some condition. To verify the effectiveness of the proposed algorithm, experiments on ORL face database are made. Experimental results show that the performance of the proposed algorithm is superior to those of some previous methods in feature extraction. Moreover, extraction of image features using the proposed algorithm needs less time than that of classical linear discriminant analysis.
Description: 7th Asian Conference on Computer Vision, ACCV 2006, Hyderabad, 13-16 January 2006
URI: http://hdl.handle.net/10397/15650
ISBN: 3540312196
9783540312192
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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