Spatial Gaussian mixture model for gender recognition

Zhen Li, Xi Zhou, Thomas S. Huang

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

Abstract

Patch-based approaches have become popular in many computer vision applications over recent years. An intrinsic flaw of this framework, missing of the spatial information, however, restricts its usage in face related applications where the spatial configuration is relatively settled. In this paper, we introduce a new patch feature representation, namely Spatial Gaussian Mixture Models (SGMM), which enhances the traditional GMM approach by taking the spatial information into consideration at both local and global scales. In the meantime, SGMM inherits all the merits of GMM, such as precise appearance description and robustness to image mis-alignment. The experiments on gender recognition demonstrate that the SGMM representation achieves more than 40% relative error reduction compared with either GMM or SVM-based approaches.

Original languageEnglish (US)
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages45-48
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - 2009
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: Nov 7 2009Nov 10 2009

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2009 IEEE International Conference on Image Processing, ICIP 2009
Country/TerritoryEgypt
CityCairo
Period11/7/0911/10/09

Keywords

  • KL-divergence
  • SGMM
  • UBM

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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