Two-dimensional PCA: a new approach to appearance-based face representation and recognition

Jian Yang, David Zhang, A. F. Frangi, Jing-yu Yang

Research output: Contribution to journalArticlepeer-review


In this paper, a new technique coined two-dimensional principal component analysis (2DPCA) is developed for image representation. As opposed to PCA, 2DPCA is based on 2D image matrices rather than 1D vectors so the image matrix does not need to be transformed into a vector prior to feature extraction. Instead, an image covariance matrix is constructed directly using the original image matrices, and its eigenvectors are derived for image feature extraction. To test 2DPCA and evaluate its performance, a series of experiments were performed on three face image databases: ORL, AR, and Yale face databases. The recognition rate across all trials was higher using 2DPCA than PCA. The experimental results also indicated that the extraction of image features is computationally more efficient using 2DPCA than PCA.
Original languageEnglish
Pages (from-to)131-137
Number of pages7
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number1
Publication statusPublished - Jan 2004


  • Principal component analysis
  • Face recognition
  • Covariance matrix
  • Feature extraction
  • Kernel
  • Independent component analysis
  • Face detection
  • Lighting
  • Image representation
  • Image databases


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