BDPCA plus LDA: A novel fast feature extraction technique for face recognition

Wangmeng Zuo, Dapeng Zhang, Jian Yang, Kuanquan Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

123 Citations (Scopus)


Appearance-based methods, especially linear discriminant analysis (LDA), have been very successful in facial feature extraction, but the recognition performance of LDA is often degraded by the so-called "small sample size" (SSS) problem. One popular solution to the SSS problem is principal component analysis (PCA) + LDA (Fisherfaces), but the LDA in other low-dimensional subspaces may be more effective. In this correspondence, we proposed a novel fast feature extraction technique, bidirectional PCA (BDPCA) plus LDA (BDPCA + LDA), which performs an LDA in the BDPCA subspace. Two face databases, the ORL and the Facial Recognition Technology (FERET) databases, are used to evaluate BDPCA + LDA. Experimental results show that BDPCA + LDA needs less computational and memory requirements and has a higher recognition accuracy than PCA + LDA.
Original languageEnglish
Pages (from-to)946-953
Number of pages8
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number4
Publication statusPublished - 1 Aug 2006


  • Bidirectional principal component analysis (BDPCA)
  • Face recognition
  • Feature extraction
  • Linear discriminant analysis (LDA)
  • Principal component analysis (PCA)

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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