通过心理驱动的面部动作协同建模，实现隐私保护下的微表情识别
FED-PsyAU: Privacy-Preserving Micro-Expression Recognition via Psychological AU Coordination and Dynamic Facial Motion Modeling
- 基于心理研究构建上下脸动作单元协同先验，指导模型学习
- 结合动态面部运动建模与联邦学习，在小样本下提升识别准确率
- 适合隐私敏感场景的微表情分析，如心理评估与安全审查
微表情是短暂、低强度且常局部发生的面部表情，能揭示个体试图隐藏的真实情绪，对犯罪审讯和心理辅导具有重要价值。然而，微表情识别面临样本少、特征细微等挑战，且实际应用中存在数据隐私问题，跨场景增强识别在隐私约束下尚未充分探索。为此，我们提出FED-PsyAU框架：首先通过心理研究建立上下脸动作单元（AUs）的协同机制，提供面部肌肉动态的结构化先验；随后设计DPK-GAT网络，融合心理先验与统计性AU模式，实现从局部到全局的层级化面部运动特征学习，有效提升识别性能；此外，联邦学习框架支持多客户端协作，无需共享数据即可提升整体识别能力，兼顾隐私保护与小样本优化。在常用微表情数据库上的实验验证了该方法的有效性。
原文摘要 · Abstract (English)
Micro-expressions (MEs) are brief, low-intensity, often localized facial expressions. They could reveal genuine emotions individuals may attempt to conceal, valuable in contexts like criminal interrogation and psychological counseling. However, ME recognition (MER) faces challenges, such as small sample sizes and subtle features, which hinder efficient modeling. Additionally, real-world applications encounter ME data privacy issues, leaving the task of enhancing recognition across settings under privacy constraints largely unexplored. To address these issues, we propose a FED-PsyAU research framework. We begin with a psychological study on the coordination of upper and lower facial action units (AUs) to provide structured prior knowledge of facial muscle dynamics. We then develop a DPK-GAT network that combines these psychological priors with statistical AU patterns, enabling hierarchical learning of facial motion features from regional to global levels, effectively enhancing MER performance. Additionally, our federated learning framework advances MER capabilities across multiple clients without data sharing, preserving privacy and alleviating the limited-sample issue for each client. Extensive experiments on commonly-used ME databases demonstrate the effectiveness of our approach.
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