Method of face recognition based on red-black wavelet transform and PCA

Yuqing He*, Huan He, Hongying Yang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

With the development of the man-machine interface and the recognition technology, face recognition has became one of the most important research aspects in the biological features recognition domain. Nowadays, PCA(Principal Components Analysis) has applied in recognition based on many face database and achieved good results. However, PCA has its limitations: the large volume of computing and the low distinction ability. In view of these limitations, this paper puts forward a face recognition method based on red-black wavelet transform and PCA. The improved histogram equalization is used to realize image preprocessing in order to compensate the illumination. Then, appling the red-black wavelet sub-band which contains the information of the original image to extract the feature and do matching. Comparing with the traditional methods, this one has better recognition rate and can reduce the computational complexity.

Original languageEnglish
Title of host publicationAdvanced Intelligent Computing Theories and Applications
Subtitle of host publicationWith Aspects of Theoretical and Methodological Issues - 4th International Conference on Intelligent Computing, ICIC 2008, Proceedings
Pages561-568
Number of pages8
DOIs
Publication statusPublished - 2008
Event4th International Conference on Intelligent Computing, ICIC 2008 - Shanghai, China
Duration: 15 Sept 200818 Sept 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5226 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Intelligent Computing, ICIC 2008
Country/TerritoryChina
CityShanghai
Period15/09/0818/09/08

Keywords

  • Face recognition
  • Improved histogram equalization
  • PCA
  • Red-black wavelet transform

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