通过分析面部关键点轨迹，提升神经疾病患者表情质量评估精度。
Trajectory-guided Motion Perception for Facial Expression Quality Assessment in Neurological Disorders
- 用关键点轨迹捕捉细微面部肌肉运动，融合视觉语义信息
- 在PFED5和多伦多神经表情数据集上分别提升6.51%和7.62%
- 适合医疗诊断辅助与神经疾病表情分析研究者使用
在神经系统疾病中，自动化面部表情质量评估对提高诊断准确性和改善患者照护至关重要，但有效捕捉面部肌肉运动的细微变化仍是挑战。本文提出分析面部关键点轨迹这一紧凑而富有信息量的表征，从高层结构视角编码这些微小运动。为此，我们引入轨迹引导的运动感知变换器（TraMP-Former），一种新型面部表情质量评估框架，将关键点轨迹特征与来自RGB帧的视觉语义线索融合，最终回归为质量评分。大量实验表明，TraMP-Former在包含PFED5和增强版Toronto NeuroFace的基准数据集上达到新最优性能，分别提升6.51%和7.62%。消融实验进一步验证了关键点轨迹在面部表情质量评估中的高效性与有效性。代码已公开于https://github.com/shuchaoduan/TraMP-Former。
原文摘要 · Abstract (English)
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.
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