Remote Photoplethysmography (rPPG) is a technology that utilizes the light absorption properties of hemoglobin, captured via camera, to analyze and measure blood volume pulse (BVP). By analyzing the measured BVP, various physiological signals such as heart rate, stress levels, and blood pressure can be derived, enabling applications such as the early prediction of cardiovascular diseases. rPPG is a rapidly evolving field as it allows the measurement of vital signals using camera-equipped devices without the need for additional devices such as blood pressure monitors or pulse oximeters, and without the assistance of medical experts. Despite extensive efforts and advances in this field, serious challenges remain, including issues related to skin color, camera characteristics, ambient lighting, and other sources of noise, which degrade performance accuracy. We argue that fair and evaluable benchmarking is urgently required to overcome these challenges and make any meaningful progress from both academic and commercial perspectives. In most existing work, models are trained, tested, and validated only on limited datasets. Worse still, some studies lack available code or reproducibility, making it difficult to fairly evaluate and compare performance. Therefore, the purpose of this study is to provide a benchmarking framework to evaluate various rPPG techniques across a wide range of datasets for fair evaluation and comparison, including both conventional non-deep neural network (non-DNN) and deep neural network (DNN) methods. GitHub URL: https://github.com/remotebiosensing/rppg.

翻译：远程光电容积描记术（rPPG）是一种利用摄像头捕获血红蛋白的光吸收特性，以分析和测量血容量脉搏（BVP）的技术。通过分析测量的BVP，可以推导出心率、压力水平和血压等多种生理信号，从而实现心血管疾病的早期预测等应用。rPPG是一个快速发展的领域，因为它允许使用配备摄像头的设备测量生命体征，无需额外设备（如血压计或脉搏血氧仪）以及医疗专家的协助。尽管该领域已有大量努力和进展，但仍面临严峻挑战，包括肤色、摄像头特性、环境光照及其他噪声源等问题，这些因素会降低性能准确性。我们认为，从学术和商业角度出发，迫切需要公平且可评估的基准测试，以克服这些挑战并取得有意义的进展。在大多数现有研究中，模型仅在有限数据集上训练、测试和验证。更糟糕的是，部分研究缺乏可用的代码或可复现性，导致难以公平评估和比较性能。因此，本研究旨在提供一个基准框架，用于在广泛数据集上评估各种rPPG技术（包括传统非深度神经网络（非DNN）和深度神经网络（DNN）方法），以实现公平评估和比较。GitHub URL：https://github.com/remotebiosensing/rppg。