Region-division vector quantization histogram method for human face recognition

Koji Kotani, Qiu Chen, Feifei Lee, Tadahiro Ohmi

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

We have developed a very simple yet highly reliable face recognition method called VQ histogram method codevector referred (or matched) count histogram, which is obtained by Vector Quantization (VQ) processing of facial image, is utilized as a very effective personal feature value. Furthermore, for adding the geometric information of the face to improve the recognition accuracy, aregion-division (RD) VQ histogram method is proposed in this paper. We divide the facial area into 5 regions relating to the facial parts (forehead, eye, nose, mouth, jaw). Recognition results with different parts are fast obtained separately and then combined by weighted averaging. Topl recognition rate of 97.4% is obtained by using FB task (1195 images) in the standard FERET database. By using the private database, which was taken in practical but yet reasonably regulated environrnent, Topl recognition rate of 100% is realized.

Original languageEnglish
Pages (from-to)257-268
Number of pages12
JournalIntelligent Automation and Soft Computing
Volume12
Issue number3
DOIs
Publication statusPublished - 2006 Jan

Keywords

  • Face recognition
  • Histogram method
  • Region-Division
  • Vector quantization (VQ)

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence

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