Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/15471
Title: An uncorrelated fisherface approach
Authors: Jing, XY
Wong, HS
Zhang, D 
Tang, YY
Keywords: Computing time
Discrimination vectors selection
Linear discrimination analysis (LDA)
Statistical uncorrelation
Uncorrelated Fisherface approach (UFA)
Issue Date: 2005
Publisher: Elsevier
Source: Neurocomputing, 2005, v. 67, no. 1-4 SUPPL., p. 328-334 How to cite?
Journal: Neurocomputing 
Abstract: The Fisherface method is the most representative method of the linear discrimination analysis (LDA) technique. However, there persists 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.
URI: http://hdl.handle.net/10397/15471
ISSN: 0925-2312
EISSN: 1872-8286
DOI: 10.1016/j.neucom.2005.01.001
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