Dynamic texture and geometry features for facial expression recognition in video

Junkai Chen, Zenghai Chen, Zheru Chi, Hong Fu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

11 Citations (Scopus)


Facial expression recognition in video has attracted growing attention recently. In this paper, we propose to handle this problem with dynamic appearance and geometric features. We propose a new feature descriptor called HOG from Three Orthogonal Planes (HOG-TOP) to represent dynamic features. In addition, we introduce two types of geometry features to represent the facial rigid changes and non-rigid changes, respectively. Multiple Kernel Learning (MKL) is applied to find an optimal combination of two types of features. And finally a Support Vector Machine (SVM) with multiple kernels is trained for the facial expression classification. Extensive experiments conducted on the extended Cohn-Kanade dataset show that our method can achieve a competitive performance compared with the other state-of-the-art methods.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
PublisherIEEE Computer Society
Number of pages5
ISBN (Electronic)9781479983391
Publication statusPublished - 9 Dec 2015
EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
Duration: 27 Sep 201530 Sep 2015


ConferenceIEEE International Conference on Image Processing, ICIP 2015
CityQuebec City


  • Facial expression recognition
  • Geometry features
  • Multiple Kernel Learning
  • texture

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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