Skip to main navigation Skip to search Skip to main content

Feature extraction based on fuzzy 2DLDA

  • Southeast University, Nanjing
  • Jinling Institute of Technology
  • Hong Kong Polytechnic University

Research output: Contribution to journalArticlepeer-review

40 Scopus citations

Abstract

In the paper, fuzzy fisherface is extended to image matrix, namely, the fuzzy 2DLDA (F2DLDA). In the proposed method, we calculate the membership degree matrix by fuzzy K-nearest neighbor (FKNN), and then incorporate the membership degree into the definition of the between-class scatter matrix and the within-class scatter matrix. Finally, we get the fuzzy between-class scatter matrix and fuzzy within-class scatter matrix. In our definition of the between-class scatter matrix and within-class matrix, the fuzzy information is better used than fuzzy fisherface. Experiments on the Yale, ORL and FERET face databases show that the new method works well.

Original languageEnglish
Pages (from-to)1556-1561
Number of pages6
JournalNeurocomputing
Volume73
Issue number10-12
DOIs
StatePublished - Jun 2010
Externally publishedYes

Keywords

  • 2DLDA
  • Face recognition
  • Feature extraction
  • Fisher
  • Fuzzy
  • LDA

Fingerprint

Dive into the research topics of 'Feature extraction based on fuzzy 2DLDA'. Together they form a unique fingerprint.

Cite this