Automatic detection of non-posed facial action units
Automatic facial expression recognition has received great attention in the past two decades due to many applications, such as developmental psychology and human-computer interface design. In most of the current studies, less attention has been paid to the recognition of non-posed facial expressions or measuring their intensity levels. In this paper, we first introduce a novel spontaneous facial expression database called DISFA, which contains videos of 27 young adults, expressing non-posed facial expressions. In this database, the absence and presence of 12 action units (AUs) as well as their intensity levels (i.e., 0-5 scale) were coded by a facial action coding system (FACS) coder. Then, we present an automatic system which can detect facial AUs described by FACS. We compared different facial representation techniques and classifiers for automatic AU detection. As a result of our experiments, we achieved 97% average detection rate using localized Gabor features with SVM classifiers.