# Remote Bio-Sensing: Open Source Benchmark Framework for Fair Evaluation of rPPG

arXiv: https://arxiv.org/abs/2307.12644 · HTML: https://mlsanity.com/paper/2307.12644/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 93 references that are in the map.
- First posted on arXiv 24 Jul 2023.

## Builds on (most cited first, 25 of 25)

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement](https://mlsanity.com/paper/2006.03790/) (2020)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformer](https://mlsanity.com/paper/2111.12082/) (2021)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [PulseGAN: Learning to generate realistic pulse waveforms in remote photoplethysmography](https://mlsanity.com/paper/2006.02699/) (2020)
- [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018)
- [MMPD: Multi-Domain Mobile Video Physiology Dataset](https://mlsanity.com/paper/2302.03840/) (2023)
- [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017)
- [PhysFormer++: Facial Video-based Physiological Measurement with SlowFast Temporal Difference Transformer](https://mlsanity.com/paper/2302.03548/) (2023)
- [Towards Racially Unbiased Skin Tone Estimation via Scene Disambiguation](https://mlsanity.com/paper/2205.03962/) (2022)
- [SCAMPS: Synthetics for Camera Measurement of Physiological Signals](https://mlsanity.com/paper/2206.04197/) (2022)
- [Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos](https://mlsanity.com/paper/2211.16922/) (2022)
- [Image Enhancement for Remote Photoplethysmography in a Low-Light Environment](https://mlsanity.com/paper/2303.09336/) (2023)
- [Face2PPG: An unsupervised pipeline for blood volume pulse extraction from faces](https://mlsanity.com/paper/2202.04101/) (2022)
- [The First Vision For Vitals (V4V) Challenge for Non-Contact Video-Based Physiological Estimation](https://mlsanity.com/paper/2109.10471/) (2021)
- [BigSmall: Efficient Multi-Task Learning for Disparate Spatial and Temporal Physiological Measurements](https://mlsanity.com/paper/2303.11573/) (2023)
- [Real-time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation](https://mlsanity.com/paper/2012.15846/) (2020)
- [iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox](https://mlsanity.com/paper/1901.04366/) (2019)
- [Camera-Based HRV Prediction for Remote Learning Environments](https://mlsanity.com/paper/2305.04161/) (2023)
- [The 1st Challenge on Remote Physiological Signal Sensing (RePSS)](https://mlsanity.com/paper/2003.11756/) (2020)
- [On the Vector Space in Photoplethysmography Imaging](https://mlsanity.com/paper/1906.04431/) (2019)
- [Exploiting Weak Head Movements for Camera-based Respiration Detection](https://mlsanity.com/paper/1906.08192/) (2019)
- [Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones](https://mlsanity.com/paper/2204.08989/) (2022)

## Built on by (most cited first, 1 of 1)

- [Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation](https://mlsanity.com/paper/2508.17924/) (2025)

## Abstract (arXiv)

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