An assembled matrix distance metric for 2DPCA-based image recognition

Wangmeng Zuo, Dapeng Zhang, Kuanquan Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

51 Citations (Scopus)

Abstract

Two-dimensional principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One characteristic 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 database and the PolyU palmprint database. The results of our experiments show that the assembled matrix distance metric is very effective in 2DPCA-based image recognition.
Original languageEnglish
Pages (from-to)210-216
Number of pages7
JournalPattern Recognition Letters
Volume27
Issue number3
DOIs
Publication statusPublished - 1 Feb 2006

Keywords

  • 2DPCA
  • Face recognition
  • Feature extraction
  • Image recognition
  • Palmprint recognition
  • PCA

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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