GraphEx: facial action unit graph for micro-expression classification

Facial micro-expressions are crucial cues for expressing human emotions. Existing works have shown substantial progress in detecting micro-expressions for various applications in the computer vision field. However, it is still onerous for existing methods to handle and interpret micro-expressions efficiently. This paper proposes a deep learning-based approach leveraging spatio-temporal and graph representation learning for micro-expression classification. We design a novel Spatial-Temporal Info Extraction Network (STIENet) for learning facial appearance and muscle motion from high dimensional video clip frames and summarizes them into more meaningful feature maps. We construct an action unit (AU) relation graph to further represent the AU co-occurrence in the same micro-expression video clip. A graph neural network (GNN) is used to learn AU-related graph embedding for the downstream classification task. Performance evaluation on two mainstream micro-expression datasets, i.e., CASME II and SAMM, show that the proposed framework outperforms other state-of-the-art methods for micro-expression classification.