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 configurable. 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.

翻译：光电容积描记（PPG）信号已成为医学、健康监测、运动等众多领域的关键技术。本研究提出了一套从人脸中稳健、可靠且可配置地提取远程PPG（rPPG）信号的流程。我们识别并评估了无监督rPPG方法关键步骤中的可行方案，在六个不同数据集上评估了当前最先进的信号处理流程，并对方法学中确保可重复性和公平比较的关键环节进行了校正。此外，我们通过三项创新拓展了该流程：1）基于刚性网格归一化的面部稳定新方法；2）动态选择面部最佳原始信号区域的新方法；3）基于QR分解的正交矩阵图像变换（OMIT）——一种增强抗压缩伪影鲁棒性的新型RGB到rPPG变换方法。实验表明，这三项改进均显著提升了从人脸恢复rPPG信号的性能，相比无监督非学习方法取得了最优结果，且在部分数据集上接近于监督学习方法。我们通过对比研究量化了每项创新的贡献，并总结了一系列对未来实现具有参考价值的观察结论。