Letter: An uncorrelated fisherface approach

Authors:
Xiao-Yuan Jing;Hau-San Wong;David Zhang;Yuan-Yan Tang
Affiliations:
Shenzhen Graduate School of Harbin Institute of Technology, Xili Town, Shenzhen, China;Department of Computer Science, City University of Hong Kong, Kowloon Tong, Hong Kong;Department of Computing, Hong Kong Polytechnic University, Kowloon, Hong Kong;Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong
Venue:
Neurocomputing
Year:
2005

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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.