Short Survey: Optical flow based analyses to detect emotion from human facial image data

Authors:
Axel Besinger;Tamara Sztynda;Sara Lal;Carmen Duthoit;Johnson Agbinya;Budi Jap;David Eager;Gamini Dissanayake
Affiliations:
Department of Medical and Molecular Biosciences, University of Technology, Sydney, Level 6, Building 4, Broadway, NSW 2007, Australia and Faculty of Engineering, University of Technology, Sydney, ...;Department of Medical and Molecular Biosciences, University of Technology, Sydney, Level 6, Building 4, Broadway, NSW 2007, Australia;Department of Medical and Molecular Biosciences, University of Technology, Sydney, Level 6, Building 4, Broadway, NSW 2007, Australia and Center for Intelligent Mechatronic Systems, University of ...;Division of Analytical Laboratories, Sydney West Area Health Service, P.O. Box 63, Penrith, NSW 2751, Australia;Faculty of Engineering, University of Technology, Sydney, Level 6, Building 2, Broadway, NSW 2007, Australia;Department of Medical and Molecular Biosciences, University of Technology, Sydney, Level 6, Building 4, Broadway, NSW 2007, Australia;Faculty of Engineering, University of Technology, Sydney, Level 6, Building 2, Broadway, NSW 2007, Australia;Faculty of Engineering, University of Technology, Sydney, Level 6, Building 2, Broadway, NSW 2007, Australia and Center for Intelligent Mechatronic Systems, University of Technology, Sydney, Broad ...
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

Citing 5
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Abstract

Artificial recognition of facial expression has attracted a lot of attention in the last few years and different facial expression detection methods have been developed. The current study uses a feature point tracking technique separately applied to the five facial image regions (eyebrows, eyes and mouth) to capture basic emotions. The used dataset contains a total 60 facial images from subject's different genders and nationality not wearing glasses and/or facial hair. Results show that the used point tracking algorithm separately applied to the five facial image regions can detect emotions in image sequences.