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 this https URL

针对帕金森病患者的面部表情质量问题，本研究提出了一种新的基于地标引导的方法 QAFE-Net，通过结合时序地标热图和 RGB 数据来捕捉面部微小肌肉动作，并将其编码和映射到严重程度评分中，实验结果表明该方法在质量评估方面超过了同类作品，也在疼痛估计方面达到了较低的平均绝对误差。

QAFE-Net：面部表情质量评估与关键点热图

QAFE-Net: Quality Assessment of Facial Expressions with Landmark  Heatmaps

Pain is a subjective experience commonly measured through patient's self
report. While there exist numerous situations in which automatic pain
estimation methods may be preferred, inter-subject variability in physiological
and behavioral pain responses has hindered the development of such methods. In
this work, we address this problem by introducing a novel personalized
multitask machine learning method for pain estimation based on individual
physiological and behavioral pain response profiles, and show its advantages in
a dataset containing multimodal responses to nociceptive heat pain.

本研究提出了一种新的个性化多任务机器学习方法，利用个体生理和行为对痛苦的反应来估计疼痛水平，并在有多种反应的数据集中，表明其优势。