2009 | OriginalPaper | Chapter
Upper Facial Action Unit Recognition
This paper concentrates on the comparisons of systems that are used for the recognition of expressions generated by six upper face action units (
AU
s) by using Facial Action Coding System (
FACS
). Haar wavelet, Haar-Like and Gabor wavelet coefficients are compared, using Adaboost for feature selection. The binary classification results by using Support Vector Machines (
SVM
) for the upper face
AU
s have been observed to be better than the current results in the literature, for example 96.5% for
AU2
and 97.6% for
AU5
. In multi-class classification case, the Error Correcting Output Coding (
ECOC
) has been applied. Although for a large number of classes, the results are not as accurate as the binary case,
ECOC
has the advantage of solving all problems simultaneously; and for large numbers of training samples and small number of classes, error rates are improved.
