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Most existing AU detection works considering AU relationships are relying onprobabilistic graphical models with manually extracted features. This paperproposes an end-to-end deep learning framework for facial AU detection withgraph convolutional network (GCN) for AU relation modeling, which has not beenexplored before. In particular, AU related regions are extracted firstly,latent representations full of AU information are learned through anauto-encoder. Moreover, each latent representation vector is feed into GCN as anode, the connection mode of GCN is determined based on the relationships ofAUs. Finally, the assembled features updated through GCN are concatenated forAU detection. Extensive experiments on BP4D and DISFA benchmarks demonstratethat our framework significantly outperforms the state-of-the-art methods forfacial AU detection. The proposed framework is also validated through a seriesof ablation studies.

Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection