An improved fast face recognition algorithm based on adjacent pixel intensity difference quantization histogram

Fei Fei Lee, Koji Kotani, Qiu Chen, Tadahiro Ohmi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

In this paper, we present an improved face recognition algorithm based on adjacent pixel intensity difference quantization (APIDQ) histogram method proposed by Kotani et al. [12]. We optimize the quantization method of APIDQ according to the maximum entropy principle (MEP), and determine the best parameters for APIDQ. Experimental results show maximum average recognition rate of 97.2 % for 400 images of 40 persons (10 images per person) from the publicly available AT&T face database.

Original languageEnglish
Title of host publicationProceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Pages316-320
Number of pages5
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR - Hong Kong, China
Duration: 2008 Aug 302008 Aug 31

Publication series

NameProceedings of the 2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
Volume1

Other

Other2008 International Conference on Wavelet Analysis and Pattern Recognition, ICWAPR
CountryChina
CityHong Kong
Period08/8/3008/8/31

Keywords

  • Adjacent pixel intensity difference quantization (APIDQ)
  • Face recognition
  • Maximum entropy principle (MEP)

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Vision and Pattern Recognition

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