Facial action units detection by robust temporal features

Abstract

Typical facial expression recognition system in computer vision field usually learns and translates facial behaviors into emotional states directly based on the training data. Since our face are not limited by a small number of class labels. In order to explain more complex facial expressions, we proposed a novel action unit (AU) detector following the Ekman's Facial Action Coding System (FACS). Our AU detection system utilized the robust temporal features and a new architecture of classification methods based on discriminative Independent Component Analysis (ICA) with whitening process by Eigenspace Method based on Class features (EMC). Therefore we can objectively describe the subtle and complex facial expressions in the same standard in psychology studies. The experimental results show the higher performance of our proposed system comparing to our previous classification methods in the standard dataset.