The muscular activities caused the activation of facial action units (AUs) when a facial expression is shown by a human face. This paper presents the methods to recognize AU using a distance feature between facial points which activates the muscles. The seven AU involved are AU1, AU4, AU6, AU12, AU15, AU17 and AU25 that characterizes a happy and sad expression. The recognition is performed on each AU according to the rules defined based on the distance of each facial point. The facial distances chosen are computed from twelve salient facial points. Then the facial distances are trained using Support Vector Machine (SVM) and Neural Network (NN). Classification result using SVM is presented with several different SVM kernels while result using NN is presented for each training, validation and testing phase. By using any SVM kernels, it is consistent that AUs that are corresponded to sad expression has a high recognition compared to happy expression. The highest average kernel performance across AUs is 93%, scored by quadratic kernel. Best results for NN across AUs is for AU25 (Lips parted) with lowest CE (0.38%) and 0% incorrect classification.
Deep Neural Network Identifies Dynamic Facial Action Units from Image Sequences
