A novel null space-based kernel discriminant analysis for face recognition

Tuo Zhao, Zhizheng Liang, Dapeng Zhang, Yahui Liu

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

2 Citations (Scopus)


The symmetrical decomposition is a powerful method to extract features for image recognition. It reveals the significant discriminative information from the mirror image of symmetrical objects. In this paper, a novel null space kernel discriminant method based on the symmetrical method with a weighted fusion strategy is proposed for face recognition. It can effectively enhance the recognition performance and shares the advantages of Null-space, kernel and symmetrical methods. The experiment results on ORL database and FERET database demonstrate that the proposed method is effective and outperforms some existing subspace methods.
Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2007, Proceedings
Number of pages10
Publication statusPublished - 1 Dec 2007
Event2007 International Conference on Advances in Biometrics, ICB 2007 - Seoul, Korea, Republic of
Duration: 27 Aug 200729 Aug 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4642 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference2007 International Conference on Advances in Biometrics, ICB 2007
Country/TerritoryKorea, Republic of


  • Face recognition
  • Symmetrical decomposition
  • Symmetrical null-space based kernel LDA
  • Weighted fusion strategy

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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