Two-dimensional fisher discriminant analysis and its application to face recognition

Zhizheng Liang, Pengfei Shi, Dapeng Zhang

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

4 Citations (Scopus)


Image matrices are often transformed into vectors prior to feature extraction, which results in the curse of dimensionality when the dimensions of matrices are huge. In order to effectively deal with this problem, a new technique for two-dimensional(2D) Fisher discriminant analysis is developed in this paper. In the proposed algorithm, the Fisher criterion function is directly constructed in terms of image matrices. Then we utilize the Fisher criterion and statistical correlation between features to construct an objective function. We theoretically analyze that the proposed algorithm is equivalent to uncorrelated two-dimensional discriminant analysis in some condition. To verify the effectiveness of the proposed algorithm, experiments on ORL face database are made. Experimental results show that the performance of the proposed algorithm is superior to those of some previous methods in feature extraction. Moreover, extraction of image features using the proposed algorithm needs less time than that of classical linear discriminant analysis.
Original languageEnglish
Title of host publicationComputer Vision - ACCV 2006 - 7th Asian Conference on Computer Vision, Proceedings
Number of pages10
Publication statusPublished - 15 Jun 2006
Event7th Asian Conference on Computer Vision, ACCV 2006 - Hyderabad, India
Duration: 13 Jan 200616 Jan 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3851 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th Asian Conference on Computer Vision, ACCV 2006

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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