Regulatory Focus Theory Induced Micro-Expression Analysis with Structured Representation Learning

Micro-expression analysis (MEA) is crucial for detecting subtle emotional cues, with applications in lie detection and psychological assessment. Existing methods struggle with three main challenges: 1) Noise sensitivity arising from the inherent subtlety of micro-expressions. 2) Reliance on fixed priors and apex annotations. 3) Information redundancy, with static features often dominating over dynamic emotional cues. To address these challenges, we propose Ac4AU, a framework inspired by Regulatory Focus Theory (RFT) that utilizes structured representation learning to decompose dynamic emotional patterns from redundant features. Specifically, AC4AU first leverages a face recognition backbone to extract robust yet redundant static representations. Secondly, a Frequency-aware Redundancy Decomposer (FRD) is introduced to eliminate the Direct Current component and retain the dynamic and process-sensitive features. Finally, a dynamic expert allocation mechanism, embodied by the AU-specific Expert Router (AUsER), is adopted to learn localized facial motion patterns and capture long-term relationships, enabling AU-targeted supervision and enhancing generalization across diverse datasets. Rigorous experiments demonstrate that the apex-free AC4AU achieves performance comparable to state-of-the-art apex-dependent methods. Additionally, we conduct a statistical analysis that provides insights into the AU dependencies. Code will be made available upon request.