IEEE J Biomed Health Inform. 2023 Nov;27(11):5530-5541. doi: 10.1109/JBHI.2023.3307942. Epub 2023 Nov 7.
Photoplethysmography (PPG) signals have become a key technology in many fields, such as medicine, well-being, or sports. Our work proposes a set of pipelines to extract remote PPG signals (rPPG) from the face robustly, reliably, and configurably. We identify and evaluate the possible choices in the critical steps of unsupervised rPPG methodologies. We assess a state-of-the-art processing pipeline in six different datasets, incorporating important corrections in the methodology that ensure reproducible and fair comparisons. In addition, we extend the pipeline by proposing three novel ideas; 1) a new method to stabilize the detected face based on a rigid mesh normalization; 2) a new method to dynamically select the different regions in the face that provide the best raw signals, and 3) a new RGB to rPPG transformation method, called Orthogonal Matrix Image Transformation (OMIT) based on QR decomposition, that increases robustness against compression artifacts. We show that all three changes introduce noticeable improvements in retrieving rPPG signals from faces, obtaining state-of-the-art results compared with unsupervised, non-learning-based methodologies and, in some databases, very close to supervised, learning-based methods. We perform a comparative study to quantify the contribution of each proposed idea. In addition, we depict a series of observations that could help in future implementations.
光电容积脉搏波（PPG）信号已成为许多领域（如医学、健康或运动）的关键技术。我们的工作提出了一组从面部稳健、可靠且可配置地提取远程 PPG 信号（rPPG）的管道。我们在无监督 rPPG 方法学的关键步骤中识别并评估了可能的选择。我们在六个不同的数据集上评估了一种最先进的处理管道，在方法学中进行了重要的校正，以确保可重现和公平的比较。此外，我们通过提出三个新的想法扩展了该管道；1）一种基于刚性网格归一化稳定检测到的面部的新方法；2）一种动态选择提供最佳原始信号的面部不同区域的新方法；以及 3）一种新的 RGB 到 rPPG 转换方法，称为基于 QR 分解的正交矩阵图像变换（OMIT），可提高对压缩伪影的鲁棒性。我们表明，所有这三个变化都能显著提高从面部中检索 rPPG 信号的能力，与无监督、非基于学习的方法相比，取得了最先进的结果，并且在某些数据库中，与基于监督、基于学习的方法非常接近。我们进行了一项对比研究，以量化每个提出的想法的贡献。此外，我们还描绘了一系列有助于未来实施的观察结果。
