Motion topological model improves detection of subtle facial movements
MoTop: Motion-Topological Model For Micro AU Detection
Computer Vision and Pattern Recognition
Summary
Facial micro-expressions are tiny and quick facial movements that often show hidden feelings. Detecting small facial muscle activations called action units (AUs) helps understand emotions better but is very tricky because many AUs look similar. The authors propose MoTop, a model that uses motion context and facial landmark patterns to spot subtle changes more accurately. It also amplifies tiny facial movements and groups facial features anatomically to improve detection. Their method outperforms previous ones on a challenging test for micro AU detection.
What this means in practice
- •For emotion recognition developers: Enhance precision of emotion detection systems by improving subtle facial muscle activation recognition.
- •For security monitoring teams: Use micro facial movement detection to better identify hidden emotional states in high-stakes environments.
Authors
Huai-Qian Khor, Mengting Wei, Yante Li, Chu Kiong Loo, Guoying Zhao
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.