An uncorrelated fisherface approach

Xiao-Yuan Jing*, Hau-San Wong, David Zhang, Yuan-Yan Tang

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

6 Citations (Scopus)

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. © 2005 Elsevier B.V. All rights reserved.
Original languageEnglish
Pages (from-to)328-334
JournalNeurocomputing
Volume67
Online published7 Mar 2005
DOIs
Publication statusPublished - Aug 2005

Research Keywords

  • Computing time
  • Discrimination vectors selection
  • Linear discrimination analysis (LDA)
  • Statistical uncorrelation
  • Uncorrelated Fisherface approach (UFA)

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