  rPPG (Remote photoplethysmography) is a technology that measures and analyzes
BVP (Blood Volume Pulse) by using the light absorption characteristics of
hemoglobin captured through a camera. Analyzing the measured BVP can derive
various physiological signals such as heart rate, stress level, and blood
pressure, which can be applied to various applications such as telemedicine,
remote patient monitoring, and early prediction of cardiovascular disease. rPPG
is rapidly evolving and attracting great attention from both academia and
industry by providing great usability and convenience as it can measure
biosignals using a camera-equipped device without medical or wearable devices.
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 and artifacts, which degrade accuracy
performance. We argue that fair and evaluable benchmarking is urgently required
to overcome these challenges and make meaningful progress from both academic
and commercial perspectives. In most existing work, models are trained, tested,
and validated only on limited datasets. Even worse, 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
