Automatic Facial Action Detection Using Histogram Variation Between Emotional States

This article presents an appearance based method to detect automatically facial actions. Our approach focuses on reducing features sensitivity to identity of the subject. We compute from an expressive image a Local Gabor Binary Pattern (LGBP) histogram and synthesize a LGBP histogram approaching the...

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Bibliographic Details
Published in2010 20th International Conference on Pattern Recognition pp. 3752 - 3755
Main Authors Senechal, Thibaud, Bailly, Kevin, Prevost, Lionel
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.08.2010
Subjects
Detectors
Encoding
Face
Histograms
Kernel
Support vector machines
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Summary:This article presents an appearance based method to detect automatically facial actions. Our approach focuses on reducing features sensitivity to identity of the subject. We compute from an expressive image a Local Gabor Binary Pattern (LGBP) histogram and synthesize a LGBP histogram approaching the one we would compute on a neutral face. Difference between these two histograms are used as inputs of Support Vector Machine (SVM) binary detectors associated with a new kernel: the Histogram Difference Intersection (HDI) kernel. Experimental results carried out for 16 Action Units (AUs) on the benchmark Cohn-Kanade database can be compared favorably with two state-of-the-art methods.
ISBN:1424475422
9781424475421
ISSN:1051-4651
DOI:10.1109/ICPR.2010.914