A novel feature extraction method using pyramid histogram of orientation gradients for smile recognition

Yang Bai, Lihua Guo, Lianwen Jin, Qinghua Huang

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

143 Scopus citations

Abstract

Recognizing smiles is of much importance for detecting happy moods. Gabor features are conventionally widely applied to facial expression recognition, but the number of Gabor features is usually too large. We proposed to use Pyramid Histogram of Oriented Gradients (PHOG) as the features extracted for smile recognition in this paper. The comparisons between the PHOG and Gabor features using a publicly available dataset demonstrated that the PHOG with a significantly shorter vector length could achieve as high a recognition rate as the Gabor features did. Furthermore, the feature selection conducted by an AdaBoost algorithm was not needed when using the PHOG features. To further improve the recognition performance, we combined these two feature extraction methods and achieved the best smile recognition rate, indicating a good value of the PHOG features for smile recognitions.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings
PublisherIEEE Computer Society
Pages3305-3308
Number of pages4
ISBN (Print)9781424456543
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE International Conference on Image Processing, ICIP 2009 - Cairo, Egypt
Duration: 7 Nov 200910 Nov 2009

Publication series

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

Conference

Conference2009 IEEE International Conference on Image Processing, ICIP 2009
Country/TerritoryEgypt
CityCairo
Period7/11/0910/11/09

Keywords

  • AdaBoost
  • Gabor feature
  • Pyramid histogram of oriented gradients
  • Smile recognition
  • Support vector machine

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