Improvement on null space LDA for face recognition: A symmetry consideration

Wangmeng Zuo, Kuanquan Wang, Dapeng Zhang

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

2 Citations (Scopus)


The approximate bilateral symmetry of human face has been explored to improve the recognition performance of some face recognition algorithms such as Linear Discriminant Analysis (LDA) and Direct-LDA (D-LDA). In this paper we summary the ways to generate virtual sample using facial symmetry, and investigate the three strategies of using facial symmetric information in the Null Space LDA (NLDA) framework. The results of our experiments indicate that, the use of facial symmetric information can further improve the recognition accuracy of conventional NLDA.
Original languageEnglish
Title of host publicationAdvances in Biometrics - International Conference, ICB 2006, Proceedings
Number of pages7
Publication statusPublished - 15 Jun 2006
EventInternational Conference on Biometrics, ICB 2006 - Hong Kong, Hong Kong
Duration: 5 Jan 20067 Jan 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3832 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Biometrics, ICB 2006
CountryHong Kong
CityHong Kong

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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