Facial expression recognition based on mixture of basic expressions and intensities

Kai-Tai Song*, Shuo Cheng Chien

*Corresponding author for this work

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

17 Scopus citations

Abstract

Facial expression recognition can provide rich emotional information for human-robot interaction. This paper presents a facial expression recognition design that recognizes facial expressions as well as intensity and mixture ratio of six basic facial expressions. In this system, Active Appearance Model (AAM) and Lucas-Kanade image alignment algorithms are adopted to align the input facial images to obtain texture features. A novel method is proposed to recognize mixture ratio of basic facial expressions and the intensity of the expression. Three kinds of texture features are used in this method: 1. texture features of the whole face, which are used as inputs of facial expression intensity recognition; 2. texture features of the upside face, which are used as inputs of upper face action units recognition; 3. texture features of the downside face, which are used as the inputs of lower face action units recognition. Back propagation neural networks are used to obtain the recognition scores, which are then exploited to classify the facial expression results. Experimental results verified that the proposed method can effectively recognize mixture ratio of six basic expressions and the expression intensity.

Original languageEnglish
Title of host publicationProceedings 2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
Pages3123-3128
Number of pages6
DOIs
StatePublished - 1 Dec 2012
Event2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012 - Seoul, Korea, Republic of
Duration: 14 Oct 201217 Oct 2012

Publication series

NameConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN (Print)1062-922X

Conference

Conference2012 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2012
CountryKorea, Republic of
CitySeoul
Period14/10/1217/10/12

Keywords

  • computer vision
  • facial expression recognition
  • human-robot interaction

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