An uncorrelated fisherface approach

Xiao Yuan Jing, Hau San Wong, Dapeng Zhang, Yuan Yan Tang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 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.
Original languageEnglish
Pages (from-to)328-334
Number of pages7
JournalNeurocomputing
Volume67
Issue number1-4 SUPPL.
DOIs
Publication statusPublished - 1 Aug 2005

Keywords

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

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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