Continuous Action Unit Intensity Modeling for Micro-Expression Recognition

Micro-Expression Recognition (MER) remains challenging due to the subtle and transient nature of facial muscle movements. While recent methods leverage Action Unit (AU) labels for MER, they often tend to ignore continuous AU intensity variations, which are critical for capturing nuanced facial expressions. To address these limitations, we propose a novel framework integrating continuous AU intensity with hierarchical motion modeling. Our approach begins with a lightweight model that regresses in-frame AU intensity values. These AU intensities are fed into our proposed Continuous AU Transformer (CAUT), which employs a temporal Transformer and a spatial Transformer to model AU evolution across frames and inter-AU dependencies. Simultaneously, a two-stage Transformer architecture extracts hierarchical optical flow features, fused with AU semantics via a multi-scale region-based fusion strategy for enhancing facial motion features. Extensive experiments demonstrate the proposed method’s state-of-the-art performance, validating the effectiveness of continuous AU intensity modeling and hierarchical feature integration for MER.