Relation Modeling with Graph Convolutional Networks for Facial Action Unit Detection
This addresses facial expression analysis for applications like human-computer interaction, with a novel approach to AU relation modeling.
The paper tackles facial action unit detection by proposing an end-to-end deep learning framework using graph convolutional networks to model AU relationships, which significantly outperforms state-of-the-art methods on BP4D and DISFA benchmarks.
Most existing AU detection works considering AU relationships are relying on probabilistic graphical models with manually extracted features. This paper proposes an end-to-end deep learning framework for facial AU detection with graph convolutional network (GCN) for AU relation modeling, which has not been explored before. In particular, AU related regions are extracted firstly, latent representations full of AU information are learned through an auto-encoder. Moreover, each latent representation vector is feed into GCN as a node, the connection mode of GCN is determined based on the relationships of AUs. Finally, the assembled features updated through GCN are concatenated for AU detection. Extensive experiments on BP4D and DISFA benchmarks demonstrate that our framework significantly outperforms the state-of-the-art methods for facial AU detection. The proposed framework is also validated through a series of ablation studies.