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.

本文提出了一种基于端到端深度学习框架进行面部 AU 检测的方法，使用图卷积网络（GCN）进行 AU 关系建模，通过解码器学习 AU 信息，再将每个隐层表示向量作为 GCN 的节点，最后将 GCN 更新的特征组合起来进行 AU 检测，实验证明该方法优于现有面部 AU 检测方法。