Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/14068
Title: An uncorrelated fisherface approach for face and palmprint recognition
Authors: Jing, XY
Lu, C
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. 3832 LNCS, p. 682-687 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: The Fisherface method is a most representative method of the linear discrimination analysis (LDA) technique. However, there persist in the Fisherface method at least two areas of weakness. The first weakness is that it cannot make the achieved discrimination vectors completely satisfy the statistical uncorrelation while costing a minimum of computing time. The second weakness is that not all the discrimination vectors are useful in pattern classification. In this paper, we propose an uncorrelated Fisherface approach (UFA) to improve the Fisherface method in these two areas. Experimental results on different image databases demonstrate that UFA outperforms the Fisherface method and the uncorrelated optimal discrimination vectors (UODV) method.
Description: International Conference on Biometrics, ICB 2006, Hong Kong, 5-7 January 2006
URI: http://hdl.handle.net/10397/14068
ISBN: 3540311114
9783540311119
ISSN: 0302-9743
EISSN: 1611-3349
Appears in Collections:Conference Paper

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