  Micro-Expression (ME) is the spontaneous, involuntary movement of a face that
can reveal the true feeling. Recently, increasing researches have paid
attention to this field combing deep learning techniques. Action units (AUs)
are the fundamental actions reflecting the facial muscle movements and AU
detection has been adopted by many researches to classify facial expressions.
However, the time-consuming annotation process makes it difficult to correlate
the combinations of AUs to specific emotion classes. Inspired by the nodes
relationship building Graph Convolutional Networks (GCN), we propose an
end-to-end AU-oriented graph classification network, namely MER-GCN, which uses
3D ConvNets to extract AU features and applies GCN layers to discover the
dependency laying between AU nodes for ME categorization. To our best
knowledge, this work is the first end-to-end architecture for Micro-Expression
Recognition (MER) using AUs based GCN. The experimental results show that our
approach outperforms CNN-based MER networks.
