Multiple-Facial Action Unit Recognition by Shared Feature Learning and Semantic Relation Modeling

In this paper, we propose multiple facial action unit recognition by modeling their relations from both features and target labels. First, a multi-task feature learning method is adopted to divide action unit recognition tasks into several groups, and then learn the shared features for each group. Second, a Bayesian network is used to model the co-existent and mutual-exclusive semantic relations among action units from the target labels of facial images. After that, the learned Bayesian network employs the recognition results of the multi-task learning, and realizes multiple facial action recognition by probabilistic inference. Experiments on the extended Cohn-Kanade database and the Denver Intensity of Spontaneous Facial Actions database demonstrate the effectiveness of our approach.