Get the FACS fast: Automated FACS face analysis benefits from the addition of velocity

Timothy R. Brick, Michael D. Hunter, Jeffrey F. Cohn

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Much progress has been made in automated facial image analysis, yet current approaches still lag behind what is possible using manual labeling of facial actions. While many factors may contribute, a key one may be the limited attention to dynamics of facial action. Most approaches classify frames in terms of either displacement from a neutral, mean face or, less frequently, displacement between successive frames (i.e. velocity). In the current paper, we evaluated the hypothesis that attention to dynamics can boost recognition rates. Using the well-known Cohn-Kanade database and support vector machines, adding velocity and acceleration decreased the number of incorrectly classified results by 14.2% and 11.2%, respectively. Average classification accuracy for the displacement and velocity classifier system across all classifiers was 90.2%. Findings were replicated using linear discriminant analysis, and found a mean decrease of 16.4% in incorrect classifications across classifiers. These findings suggest that information about the dynamics of a movement, that is, the velocity and to a lesser extent the acceleration of a change, can helpfully inform classification of facial expressions.

Original languageEnglish (US)
Title of host publicationProceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009
DOIs
StatePublished - 2009
Event2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009 - Amsterdam, Netherlands
Duration: Sep 10 2009Sep 12 2009

Publication series

NameProceedings - 2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009

Other

Other2009 3rd International Conference on Affective Computing and Intelligent Interaction and Workshops, ACII 2009
CountryNetherlands
CityAmsterdam
Period9/10/099/12/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction
  • Software

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