Dynamic texture and geometry features for facial expression recognition in video

Junkai CHEN, Zenghai CHEN, Zheru CHI, Hong FU

Research output: Chapter in Book/Report/Conference proceedingChapters

13 Citations (Scopus)

Abstract

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. Copyright © 2015 IEEE.
Original languageEnglish
Title of host publication2015 IEEE International Conference on Image Processing (ICIP 2015)
Place of PublicationPiscataway, NJ
PublisherIEEE
Pages4967-4971
ISBN (Electronic)9781479983391, 9781479983384, 147998339X
ISBN (Print)9781479983407
DOIs
Publication statusPublished - 2015

Citation

Chen, J., Chen, Z., Chi, Z., & Fu, H. (2015). Dynamic texture and geometry features for facial expression recognition in video. In 2015 IEEE International Conference on Image Processing (ICIP 2015) (pp. 4967-4971). Piscataway, NJ: IEEE.

Keywords

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

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