Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition

Jian Yang, Dapeng Zhang, Alejandro F. Frangi, Jing Yu Yang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2870 Citations (Scopus)

Abstract

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
Volume26
Issue number1
DOIs
Publication statusPublished - 1 Jan 2004

Keywords

  • Eigenfaces
  • Face recognition
  • Feature extraction
  • Image representation
  • Principal Component Analysis (PCA)

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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