Impact of action unit detection in automatic emotion recognition

In this paper, we investigate the interest of action unit (AU) detection for automatic emotion recognition. We propose and compare two emotion detectors: the first works directly on a high-dimensional feature space and the second projects facial image in the low-dimensional space of AU intensities b...

Full description

Saved in:
Bibliographic Details
Published in:Pattern analysis and applications : PAA 2014-02, Vol.17 (1), p.51-67
Main Authors: Senechal, Thibaud, Bailly, Kevin, Prevost, Lionel
Format: Article
Language:English
Subjects:
Anatomical correlates of behavior
Applied sciences
Artificial intelligence
Behavioral psychophysiology
Biological and medical sciences
Computer Science
Computer science
control theory
systems
Computer Vision and Pattern Recognition
Data processing. List processing. Character string processing
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Memory organisation. Data processing
Pattern Recognition
Pattern recognition. Digital image processing. Computational geometry
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Software
Theoretical Advances
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we investigate the interest of action unit (AU) detection for automatic emotion recognition. We propose and compare two emotion detectors: the first works directly on a high-dimensional feature space and the second projects facial image in the low-dimensional space of AU intensities before recognizing emotion. In both approaches, facial images are coded by local Gabor binary pattern (LGBP) histogram differences. These features reduce the sensitivity to subject identity by computing differences between two LGBP histograms: one computed on an expressive image and the other synthesized and approaching the one we would compute on a neutral face of the same subject. As classifiers, we test support vector machines with different kernels. A new kernel is proposed, the histogram difference intersection kernel that increases classification performances. This kernel is well suited when exploiting the proposed histogram differences. Thorough experiments on three challenging databases (respectively, the Cohn-Kanade, MMI and Bosphorus databases) show the accuracy of our AU and emotion detectors. They lead to significant conclusions on three critical issues: (1) the interest of combining different training databases labeled by different AU coders, (2) the influence of each AU according to its type and detection accuracy on emotion recognition and (3) the sensitivity to identity variations.
ISSN:1433-7541
1433-755X
DOI:10.1007/s10044-012-0279-5