Local binary pattern on hexagonal structure for face matching

Xiangjian He, Tom Hintz, Jianmin Li, Huaifeng Zhang, Qiang Wu, Wenjing Jia

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

Abstract

Principal Components Analysis (PCA), Independent Component Analysis (ICA) and Linear Discriminant Analysis (LDA), have been widely used for 2D face recognition. Local Binary Pattern (LBP), however, provides a simpler and more effective way to represent faces. With LBP, face image is divided into small regions from which LBP histograms are extracted and concatenated into a single and global feature histogram representing the face image. The recognition is performed using Chi square and other commonly used dissimilarity measures. In this paper, we construct LBP codes together with three dissimilarity measures on hexagonal structure. We show that LBPs defined on hexagonal structure will lead to a faster and more accurate scheme for face recognition.

Original languageEnglish
Title of host publicationProceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
Pages455-460
Number of pages6
Publication statusPublished - 2007
Externally publishedYes
Event2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007 - Las Vegas, NV, United States
Duration: 25 Jun 200728 Jun 2007

Publication series

NameProceedings of the 2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007

Conference

Conference2007 International Conference on Image Processing, Computer Vision, and Pattern Recognition, IPCV 2007
Country/TerritoryUnited States
CityLas Vegas, NV
Period25/06/0728/06/07

Keywords

  • Chi square measure
  • Face recognition
  • Hexagonal structure
  • Local binary patterns

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Vision and Pattern Recognition

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