# Assessment of Deep Learning-based Heart Rate Estimation using Remote Photoplethysmography under Different Illuminations

arXiv: https://arxiv.org/abs/2107.13193 · HTML: https://mlsanity.com/paper/2107.13193/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 28 Jul 2021.

## Builds on (most cited first, 13 of 13)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement](https://mlsanity.com/paper/2001.06826/) (2020)
- [DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networks](https://mlsanity.com/paper/1805.07888/) (2018)
- [Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks](https://mlsanity.com/paper/1905.02419/) (2019)
- [RhythmNet: End-to-end Heart Rate Estimation from Face via Spatial-temporal Representation](https://mlsanity.com/paper/1910.11515/) (2019)
- [Remote Heart Rate Measurement from Highly Compressed Facial Videos: an End-to-end Deep Learning Solution with Video Enhancement](https://mlsanity.com/paper/1907.11921/) (2019)
- [Video-based Remote Physiological Measurement via Cross-verified Feature Disentangling](https://mlsanity.com/paper/2007.08213/) (2020)
- [Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset](https://mlsanity.com/paper/1908.00682/) (2019)
- [VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Video](https://mlsanity.com/paper/1810.04927/) (2018)
- [Meta-rPPG: Remote Heart Rate Estimation Using a Transductive Meta-Learner](https://mlsanity.com/paper/2007.06786/) (2020)
- [A Reproducible Study on Remote Heart Rate Measurement](https://mlsanity.com/paper/1709.00962/) (2017)
- [Analysis of CNN-based remote-PPG to understand limitations and sensitivities](https://mlsanity.com/paper/1911.02736/) (2019)
- [iPhys: An Open Non-Contact Imaging-Based Physiological Measurement Toolbox](https://mlsanity.com/paper/1901.04366/) (2019)

## Built on by (most cited first, 6 of 6)

- [Perfusion assessment via local remote photoplethysmography (rPPG)](https://mlsanity.com/paper/2208.13840/) (2022)
- [Efficient Deep Learning-based Estimation of the Vital Signs on Smartphones](https://mlsanity.com/paper/2204.08989/) (2022)
- [Generalization of Video-Based Heart Rate Estimation Methods To Low Illumination and Elevated Heart Rates](https://mlsanity.com/paper/2503.11697/) (2025)
- [FastBVP-Net: a lightweight pulse extraction network for measuring heart rhythm via facial videos](https://mlsanity.com/paper/2206.12558/) (2022)
- [R2I-rPPG: A Robust Region of Interest Selection Method for Remote Photoplethysmography to Extract Heart Rate](https://mlsanity.com/paper/2410.15851/) (2024)
- [Illumination-Robust Camera-Based Heart-Rate Estimation for Physiological Sensing in Robots](https://mlsanity.com/paper/2606.12378/) (2026)

## Abstract (arXiv)

Remote photoplethysmography (rPPG) monitors heart rate without requiring physical contact, which allows for a wide variety of applications. Deep learning-based rPPG have demonstrated superior performance over the traditional approaches in controlled context. However, the lighting situation in indoor space is typically complex, with uneven light distribution and frequent variations in illumination. It lacks a fair comparison of different methods under different illuminations using the same dataset. In this paper, we present a public dataset, namely the BH-rPPG dataset, which contains data from thirty five subjects under three illuminations: low, medium, and high illumination. We also provide the ground truth heart rate measured by an oximeter. We evaluate the performance of three deep learning-based methods (Deepphys, rPPGNet, and Physnet) to that of four traditional methods (CHROM, GREEN, ICA, and POS) using two public datasets: the UBFC-rPPG dataset and the BH-rPPG dataset. The experimental results demonstrate that traditional methods are generally more resistant to fluctuating illuminations. We found that the Physnet achieves lowest mean absolute error (MAE) among deep learning-based method under medium illumination, whereas the CHROM achieves 1.04 beats per minute (BPM), outperforming the Physnet by 80%. Additionally, we investigate potential methods for improving performance of deep learning-based methods. We find that brightness augmentation make model more robust to variation illumination. These findings suggest that while developing deep learning-based heart rate estimation algorithms, illumination variation should be taken into account. This work serves as a benchmark for rPPG performance evaluation and it opens a pathway for future investigation into deep learning-based rPPG under illumination variations.