MoTop：用于微表情动作单元检测的运动拓扑模型

中文摘要 AI 辅助

面部微表情是自发的、短暂的且微妙的面部运动，能够揭示高风险环境中的压抑情绪。与经典的表情分析相比，检测动作单元（AU）能对面部运动提供更精细的表示，是定义表情类别及其他下游任务之前的初步步骤。因此，它在面部分析中是一项关键的上游任务，改进AU检测模块能够提升面部分析的精度。尽管如此，由于AU激活区域的局限性，AU检测具有挑战性，这导致了不同AU之间的混淆，即所谓的AU歧义。为了建模这种细粒度的变化，我们提出了\ extbf{MoTop}，一种运动拓扑模型，该模型通过可学习的运动上下文进行增强，为面部活动提供区域性的软引导，随后利用面部关键点捕捉微AU的细粒度拓扑变化。为了增强微面部关键点的表示，我们通过线性外推放大编码后的面部关键点转移，从而增加关键点的空间邻近性，并增强低强度关键点动态。此外，我们设计了解剖学面部聚类，以增强层次化表示，促进面部几何的多尺度建模，并改善微拓扑表示。凭借这些贡献，我们在CD6ME协议上针对微AU检测任务取得了最先进的性能。

英文摘要

Facial micro-expressions are spontaneous, brief, and subtle facial movements that reveal suppressed emotions in high-stakes environments. In contrast to classic expression analysis, detecting action unit (AU) yields a finer representation of facial movements, serving as a preliminary step before defining expression classes and other downstream tasks. Therefore, it represents a crucial upstream task in facial analysis, and improving an AU detection module increases the precision of facial analysis. Despite that, detecting AU is challenging because of the constrictive nature of the AU activation regions, leading to confusion among different AUs known as AU ambiguity. To model the fine-scale changes, we propose \textbf{MoTop}, a motion-topological model that is augmented with a learnable motion context, yielding regional soft guidance for facial activity, followed by facial landmarks that capture the fine-scale topological changes of micro AUs. To increase the micro facial landmark representations, we amplify the encoded facial landmark transitions via linear extrapolation, thereby increasing the spatial proximity of landmarks and enhancing the low-intensity landmark dynamics. In addition, we design anatomical facial clusters that enhance the hierarchical representation, facilitating multi-scale modelling of facial geometry and improving micro-topological representations. With these contributions, we have achieved state-of-the-art performance on the CD6ME protocol for the micro AU detection task.

发表机构

• University of Oulu（奥卢大学）
• CAAS（中国农业科学院）
• University of Malaya（马来亚大学）
• ELLIS Institute Finland（芬兰ELLIS研究所）

机构由 AI 辅助整理，请以论文原文为准。