Bidirectional PCA with assembled matrix distance metric for image recognition

Wangmeng Zuo, Dapeng Zhang, Kuanquan Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

116 Citations (Scopus)


Principal component analysis (PCA) has been very successful in image recognition. Recent research on PCA-based methods has mainly concentrated on two issues, namely: 1) feature extraction and 2) classification. This paper proposes to deal with these two issues simultaneously by using bidirectional PCA (BD-PCA) supplemented with an assembled matrix distance (AMD) metric. For feature extraction, BD-PCA is proposed, which can be used for image feature extraction by reducing the dimensionality in both column and row directions. For classification, an AMD metric is presented to calculate the distance between two feature matrices and then the nearest neighbor and nearest feature line classifiers are used for image recognition. The results of the experiments show the efficiency of BD-PCA with AMD metric in image recognition.
Original languageEnglish
Pages (from-to)863-872
Number of pages10
JournalIEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Issue number4
Publication statusPublished - 1 Aug 2006


  • Face recognition
  • Feature extraction
  • Image recognition
  • Nearest feature line
  • Palm print recognition
  • 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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