Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders

2025-04-13

AI Summary

Addressing the challenge of modeling subtle facial muscle movements in facial expression quality assessment (FEQA) for neurological disorder diagnosis, this paper proposes TraMP-Former—a trajectory-guided motion-aware Transformer architecture. To capture fine-grained dynamic facial behavior, TraMP-Former uniquely fuses structured facial landmark trajectories with RGB semantic features via trajectory encoding, multimodal alignment, spatiotemporal attention, and a regression-oriented Transformer decoder, enabling end-to-end prediction of FEQA scores. Evaluated on PFED5 and an enhanced Toronto NeuroFace dataset, it achieves new state-of-the-art performance, improving accuracy by 6.51% and 7.62%, respectively. Ablation studies quantitatively validate that explicit modeling of landmark trajectories yields substantial gains for FEQA, confirming their critical role in characterizing neuromotor deficits.

Technology Category

• Computer Vision: Motion & Tracking
• Machine Learning: Feature Construction/Reformulation
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

Automated facial expression quality assessment (FEQA) in neurological disorders is critical for enhancing diagnostic accuracy and improving patient care, yet effectively capturing the subtle motions and nuances of facial muscle movements remains a challenge. We propose to analyse facial landmark trajectories, a compact yet informative representation, that encodes these subtle motions from a high-level structural perspective. Hence, we introduce Trajectory-guided Motion Perception Transformer (TraMP-Former), a novel FEQA framework that fuses landmark trajectory features for fine-grained motion capture with visual semantic cues from RGB frames, ultimately regressing the combined features into a quality score. Extensive experiments demonstrate that TraMP-Former achieves new state-of-the-art performance on benchmark datasets with neurological disorders, including PFED5 (up by 6.51%) and an augmented Toronto NeuroFace (up by 7.62%). Our ablation studies further validate the efficiency and effectiveness of landmark trajectories in FEQA. Our code is available at https://github.com/shuchaoduan/TraMP-Former.

Problem

• Assessing facial expression quality in neurological disorders
• Capturing subtle facial muscle movements accurately
• Improving diagnostic accuracy through automated FEQA

Innovation

• Uses facial landmark trajectory analysis
• Integrates motion and visual semantic cues
• Achieves state-of-the-art FEQA performance