Laplacian bidirectional PCA for face recognition

Wankou Yang, Changyin Sun, Lei Zhang, Karl Ricanek

Research output: Journal article publicationJournal articleAcademic researchpeer-review

24 Citations (Scopus)


Two-dimensional principal components analysis (2DPCA) needs more coefficients than principal components analysis (PCA) for image representation and hence needs more time for classification. The bidirectional PCA (BDPCA) is proposed to overcome these drawbacks of 2DPCA. Both 2DPCA and BDPCA, however, can work only in Euclidean space. In this paper, we propose Laplacian BDPCA (LBDPCA) to enhance the robustness of BDPCA by extending it to non-Euclidean space. Experimental results on representative face databases show that LBDPCA works well and it surpasses BDPCA.
Original languageEnglish
Pages (from-to)487-493
Number of pages7
Issue number1-3
Publication statusPublished - 1 Dec 2010


  • 2DPCA
  • Face recognition
  • Laplacian

ASJC Scopus subject areas

  • Computer Science Applications
  • Cognitive Neuroscience
  • Artificial Intelligence


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