How many frames does facial expression recognition require?

Kaimin Yu, Zhiyong Wang, Genliang Guan, Qiuxia Wu, Zheru Chi, Dagan Feng

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

1 Citation (Scopus)

Abstract

Facial expression analysis is essential to enable socially intelligent processing of multimedia video content. Most facial expression recognition algorithms generally analyze the whole image sequence of an expression to exploit its temporal characteristics. However, it is seldom studied whether it is necessary to utilize all the frames of a sequence, since human beings are able to capture the dynamics of facial expressions from very short sequences (even only one frame). In this paper, we investigate the impact of the number of frames in a facial expression sequence on facial expression recognition accuracy. In particular, we develop a key frame selection method through key point based frame representation. Experimental results on the popular CK facial expression dataset indicate that recognition accuracy achieved with half of the sequence frames is comparable to that of utilizing all the sequence frames. Our key frame selection method can further reduce the number of frames without clearly compromising recognition accuracy.
Original languageEnglish
Title of host publicationProceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012
Pages290-295
Number of pages6
DOIs
Publication statusPublished - 4 Oct 2012
Event2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012 - Melbourne, VIC, Australia
Duration: 9 Jul 201213 Jul 2012

Conference

Conference2012 IEEE International Conference on Multimedia and Expo Workshops, ICMEW 2012
Country/TerritoryAustralia
CityMelbourne, VIC
Period9/07/1213/07/12

Keywords

  • Facial expression recognition
  • Facial expression representation
  • Keyframe
  • LBP-TOP

ASJC Scopus subject areas

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

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