Uncorrelated projection discriminant analysis and its application to face image feature extraction

Jian Yang, Jing Yu Yang, Alejandro F. Frangi, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

30 Citations (Scopus)

Abstract

In this paper, a novel image projection analysis method (UIPDA) is first developed for image feature extraction. In contrast to Liu's projection discriminant method, UIPDA has the desirable property that the projected feature vectors are mutually uncorrelated. Also, a new LDA technique called EULDA is presented for further feature extraction. The proposed methods are tested on the ORL and the NUST603 face databases. The experimental results demonstrate that: (i) UIPDA is superior to Liu's projection discriminant method and more efficient than Eigenfaces and Fisherfaces; (ii) EULDA outperforms the existing PCA plus LDA strategy; (iii) UIPDA plus EULDA is a very effective two-stage strategy for image feature extraction.
Original languageEnglish
Pages (from-to)1325-1347
Number of pages23
JournalInternational Journal of Pattern Recognition and Artificial Intelligence
Volume17
Issue number8
DOIs
Publication statusPublished - 1 Dec 2003

Keywords

  • Eigenfaces
  • Face recognition
  • Feature extraction
  • Fisherfaces
  • Linear discriminant analysis (LDA)

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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