Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders
This work addresses the challenge of capturing subtle facial motions for improved diagnostic accuracy in neurological disorders, representing an incremental advancement in FEQA methods.
The paper tackles the problem of automated facial expression quality assessment (FEQA) in neurological disorders by proposing TraMP-Former, a framework that fuses landmark trajectory features with visual semantic cues, achieving state-of-the-art performance with improvements of up to 6.51% on PFED5 and 7.62% on an augmented Toronto NeuroFace dataset.
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.