Intra- and Inter-Contrastive Learning for Micro-expression Action Unit Detection
Encoding facial expressions via Action Units (AUs) has been found effective for resolving the ambiguity issue among different expressions. In the literature, AU detection has extensive researches in macro-expressions. However, there is limited research about AU analysis for micro-expressions (MEs). Micro-expression Action Unit (MEAU) detection becomes a challenging problem because of the subtle facial motion. To alleviate this problem, in this paper, we study the contrastive learning for modeling subtle AUs and propose a novel MEAU detection method by learning the intra- and inter-contrastive information among MEs. Through the intra-contrastive learning module, the difference between the onset and apex frames is enlarged and utilized to obtain the discriminative representation for low-intensity AU detection. In addition, considering the subtle difference between MEAUs, the inter-contrastive learning is designed to automatically explore and enlarge the difference between different AUs to enhance the MEAU detection robustness. Intensive experiments on two widely used ME databases have demonstrated the effectiveness and generalization ability of our proposed method.