Binary two-dimensional PCA

Yanwei Pang*, Dacheng Tao, Yuan Yuan, Xuelong Li

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Fast training and testing procedures are crucial in biometrics recognition research. Conventional algorithms, e.g., principal component analysis (PCA), fail to efficiently work on large-scale and high-resolution image data sets. By incorporating merits from both two-dimensional PCA (2DPCA)-based image decomposition and fast numerical calculations based on Haarlike bases, this technical correspondence first proposes binary 2DPCA (B-2DPCA). Empirical studies demonstrated the advantages of B-2DPCA compared with 2DPCA and binary PCA.

Original languageEnglish
Pages (from-to)1176-1180
Number of pages5
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number4
Publication statusPublished - 1 Aug 2008


  • 2-D PCA (2DPCA)
  • Face recognition
  • Haarlike bases
  • Principal component analysis (PCA)
  • Subspace selection


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