MoTop: Motion-Topological Model For Micro AU Detection

2026-09-25

AI Summary

This study addresses the ambiguity of action units (AUs) in micro-expression detection caused by spatially restricted activation regions. To this end, it proposes a motion topology model that integrates learnable motion context with facial landmarks to precisely capture fine-grained topological variations of subtle AUs. The core innovations include introducing landmark linear extrapolation to enhance dynamic feature representation and designing anatomical facial clusters to optimize multi-scale geometric modeling. Experimental results demonstrate that the proposed method achieves state-of-the-art performance on the micro-AU detection task under the CD6ME protocol.

Abstract

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.

Problem

• Micro AU Detection
• Action Unit Ambiguity
• Facial Micro-expressions
• Fine-scale Changes

Innovation

• Micro AU Detection
• Motion-Topological Model
• Facial Landmarks
• Linear Extrapolation
• Anatomical Facial Clusters