Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Wang Meng Zuo, Kuan Quan Wang, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)


Two-dimensional Principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One advantage of 2DPCA is that it can extract feature matrix using a straightforward image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two image databases, the ORL face and the PolyU palmprint. The experimental results show that the assembled matrix distance metric is very effective in 2DPCA based image recognition.
Original languageEnglish
Title of host publication2005 International Conference on Machine Learning and Cybernetics, ICMLC 2005
Number of pages6
Publication statusPublished - 12 Dec 2005
EventInternational Conference on Machine Learning and Cybernetics, ICMLC 2005 - Guangzhou, China
Duration: 18 Aug 200521 Aug 2005


ConferenceInternational Conference on Machine Learning and Cybernetics, ICMLC 2005


  • 2DPCA
  • Assemble Matrix Metric
  • Face Recognition
  • Image Recognition
  • Palmprint Recognition

ASJC Scopus subject areas

  • Engineering(all)

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