Monitoring physiological changes [e.g., heart rate (HR), respiration, and HR variability (HRV)] is important for measuring human emotions. Physiological responses are more reliable and harder to alter compared to explicit behaviors (such as facial expressions and speech), but they require special contact sensors to obtain. Research in the last decade has shown that photoplethysmography (PPG) signals can be remotely measured (rPPG) from facial videos under ambient light, from which physiological changes can be extracted. This promising finding has attracted much interest from researchers, and the field of rPPG measurement has been growing fast. In this article, we review current progress on intelligent signal processing approaches for rPPG measurement, including earlier works on unsupervised approaches and recently proposed supervised models, benchmark data sets, and performance evaluation. We also review studies on rPPG-based affective applications and compare them with other affective computing modalities. We conclude this article by emphasizing the current main challenges and highlighting future directions.

中文翻译：

---

基于面部视频的生理信号测量：最新进展和情感应用

监测生理变化 [例如，心率 (HR)、呼吸和 HR 变异性 (HRV)] 对于测量人类情绪很重要。与外显行为（例如面部表情和言语）相比，生理反应更可靠且更难改变，但它们需要特殊的接触传感器才能获得。过去十年的研究表明，光体积描记 (PPG) 信号可以从环境光下的面部视频进行远程测量 (rPPG)，从中可以提取生理变化。这一有希望的发现引起了研究人员的极大兴趣，rPPG 测量领域发展迅速。在本文中，我们回顾了用于 rPPG 测量的智能信号处理方法的当前进展，包括早期关于无监督方法的工作和最近提出的监督模型，基准数据集和性能评估。我们还回顾了基于 rPPG 的情感应用的研究，并将它们与其他情感计算方式进行了比较。我们通过强调当前的主要挑战并突出未来的方向来结束本文。