A parameterized direct LDA and its application to face recognition

Fengxi Song, Dapeng Zhang, Jizhong Wang, Hang Liu, Qing Tao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)


In this paper, we propose a new feature extraction method-parameterized direct linear discriminant analysis (PD-LDA) for small sample size problems. Similar to direct LDA (D-LDA), PD-LDA is a modification of KLB (the Karhunen-Loève expansion based on the between-class scatter matrix). As an improvement of D-LDA and KLB, PD-LDA inherits two important advantages of them. That is, it can be directly applied to high-dimensional input spaces and implemented with great efficiency. Meanwhile, experimental results conducted on two benchmark face image databases, i.e., AR and FERET, demonstrate that PD-LDA is much more effective and robust than D-LDA. In addition, it outperforms state-of-the-art facial feature extraction methods such as KLB, eigenfaces, and Fisherfaces.
Original languageEnglish
Pages (from-to)191-196
Number of pages6
Issue number1-3
Publication statusPublished - 1 Dec 2007


  • Direct linear discriminant analysis
  • Feature extraction
  • Karhunen-Loève expansion
  • Large-scale face recognition
  • Weight coefficient

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence

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