QAFE-Net：基于特征点热图的面部表情质量评估
QAFE-Net: Quality Assessment of Facial Expressions with Landmark Heatmaps
AI总结：
针对健康应用中面部表情质量评估需求，提出结合时序特征点热图与RGB数据的QAFE-Net方法，在帕金森病表情数据集和疼痛基准上均优于现有最优方法。
摘要：
面部表情识别（FER）方法在对人类情绪与感受进行分类方面已取得重大进展。除FER外，疼痛评估方法可衡量疼痛表情的强度等级，但对所有面部表情的质量进行评估在健康相关应用中具有关键价值。本研究针对帕金森病患者的五种不同面部表情的质量评估问题展开研究。我们提出了一种全新的特征点引导方法QAFE-Net，该方法将时序特征点热图与RGB数据相结合，以捕捉细微的面部肌肉运动，对这些运动进行编码并映射为严重程度评分。我们在新构建的帕金森病面部表情数据集（PFED5）以及疼痛评估基准UNBC-McMaster肩部疼痛表情档案数据库上对所提方法进行了评估。对比实验表明，该方法在PFED5上的表现优于当前最优（SOTA）动作质量评估方法，在UNBC-McMaster上的平均绝对误差低于当前最优疼痛评估方法。我们的代码和新的PFED5数据集已发布于https://github.com/shuchaoduan/QAFE-Net。
英文摘要：
Facial expression recognition (FER) methods have made great inroads in categorising moods and feelings in humans. Beyond FER, pain estimation methods assess levels of intensity in pain expressions, however assessing the quality of all facial expressions is of critical value in health-related applications. In this work, we address the quality of five different facial expressions in patients affected by Parkinson's disease. We propose a novel landmark-guided approach, QAFE-Net, that combines temporal landmark heatmaps with RGB data to capture small facial muscle movements that are encoded and mapped to severity scores. The proposed approach is evaluated on a new Parkinson's Disease Facial Expression dataset (PFED5), as well as on the pain estimation benchmark, the UNBC-McMaster Shoulder Pain Expression Archive Database. Our comparative experiments demonstrate that the proposed method outperforms SOTA action quality assessment works on PFED5 and achieves lower mean absolute error than the SOTA pain estimation methods on UNBC-McMaster. Our code and the new PFED5 dataset are available at https://github.com/shuchaoduan/QAFE-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
