Abstract
In this paper, we propose multiple facial Action Unit (AU) recognition and intensity estimation by modeling their relations in both feature and label spaces. First, a multi-task feature learning method is adopted to learn the shared features among the group of facial action units, and recognize or estimate their intensity simultaneously. Second, a Bayesian network is used to model the co-existent and mutual-exclusive semantic relations among action units. Finally, through probabilistic inference, the learned Bayesian network combines the results of the multi-task learning with the AU relations it captures to perform multiple AU recognition and AU intensity estimation. Experiments on the extended Cohn-Kanade database, the MMI database, the McMaster database and the DISFA database demonstrate the effectiveness of our method for both AU classification and AU intensity estimation.
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