VQ-based face recognition algorithm using code pattern classification and Self-Organizing Maps

Qiu Chen, Koji Kotani, Feifei Lee, Tadahiro Ohmi

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

2 Citations (Scopus)

Abstract

In this paper, an improved codebook design method is proposed for VQ-based fast face recognition algorithm to improve recognition accuracy. Combined by a systematically organized codebook based on the classification of code patterns abstracted from facial images and another codebook created by Kohonen 's Self-Organizing Maps (SOM) method, an optimized codebook consisted of 2x2 codevectors for facial images is generated. The performance of proposed algorithm is demonstrated by using publicly available AT&T database containing variations in lighting, posing, and expressions. Compared with the algorithms employing original codebook or SOM codebook separately, experimental results show face recognition using proposed codebook is more efficient. The highest average recognition rate of 98.6% is obtained for 40 persons' 400 images of AT&T database.

Original languageEnglish
Title of host publication2008 9th International Conference on Signal Processing, ICSP 2008
Pages2059-2064
Number of pages6
DOIs
Publication statusPublished - 2008 Dec 1
Event2008 9th International Conference on Signal Processing, ICSP 2008 - Beijing, China
Duration: 2008 Oct 262008 Oct 29

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP

Other

Other2008 9th International Conference on Signal Processing, ICSP 2008
CountryChina
CityBeijing
Period08/10/2608/10/29

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Science Applications

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