Face recognition based on 2D Fisherface approach

Xiao Yuan Jing, Hau San Wong, Dapeng Zhang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

89 Citations (Scopus)


Two-dimensional (2D) discrimination analysis using methods such as 2D PCA and Image LDA is of interest in face recognition because it extracts discriminative features faster than one-dimensional (1D) discrimination analysis. However, existing 2D methods generally use more discriminative features and take longer to test than 1D methods. 2D PCA in particular cannot make full use of the Fisher discriminant criterion. Image LDA also has drawbacks in that it cannot perform 2D principal component analysis and discards components with poor discriminative capabilities. In addition, existing 2D methods cannot provide an automatic strategy to choose 2D principal components or discriminant vectors. In this paper, we propose 2D Fisherface, a novel discrimination approach that combines the two-stage "PCA+LDA" strategy and 2D discrimination techniques. It can extract face discriminative features by automatically selecting two-dimensional principal components and discriminant vectors. Using the AR database as the test data, it is shown that the proposed approach is faster and more effective than several representative 1D and 2D discrimination methods.
Original languageEnglish
Pages (from-to)707-710
Number of pages4
JournalPattern Recognition
Issue number4
Publication statusPublished - 1 Apr 2006


  • 2D discriminant vector
  • 2D principal component
  • Discriminative feature extraction
  • Two-dimensional (2D) Fisherface approach

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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