# Analysis of CNN-based remote-PPG to understand limitations and sensitivities

arXiv: https://arxiv.org/abs/1911.02736 · HTML: https://mlsanity.com/paper/1911.02736/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2019.

## Built on by (most cited first, 19 of 19)

- [rPPG-Toolbox: Deep Remote PPG Toolbox](https://mlsanity.com/paper/2210.00716/) (2022)
- [Non-Contrastive Unsupervised Learning of Physiological Signals from Video](https://mlsanity.com/paper/2303.07944/) (2023)
- [rPPG-MAE: Self-supervised Pre-training with Masked Autoencoders for Remote Physiological Measurement](https://mlsanity.com/paper/2306.02301/) (2023)
- [Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos](https://mlsanity.com/paper/2211.16922/) (2022)
- [Face2PPG: An unsupervised pipeline for blood volume pulse extraction from faces](https://mlsanity.com/paper/2202.04101/) (2022)
- [Vision Transformers in Medical Computer Vision -- A Contemplative Retrospection](https://mlsanity.com/paper/2203.15269/) (2022)
- [Deception Detection and Remote Physiological Monitoring: A Dataset and Baseline Experimental Results](https://mlsanity.com/paper/2106.06583/) (2021)
- [Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss](https://mlsanity.com/paper/2203.10882/) (2022)
- [Synthetic Data for Multi-Parameter Camera-Based Physiological Sensing](https://mlsanity.com/paper/2110.04902/) (2021)
- [Assessment of Deep Learning-based Heart Rate Estimation using Remote Photoplethysmography under Different Illuminations](https://mlsanity.com/paper/2107.13193/) (2021)
- [RGB Camera-based Physiological Sensing: Challenges and Future Directions](https://mlsanity.com/paper/2110.13362/) (2021)
- [SiNC+: Adaptive Camera-Based Vitals with Unsupervised Learning of Periodic Signals](https://mlsanity.com/paper/2404.13449/) (2024)
- [Evaluation of Video-Based rPPG in Challenging Environments: Artifact Mitigation and Network Resilience](https://mlsanity.com/paper/2405.01230/) (2024)
- [Remote Pulse Estimation in the Presence of Face Masks](https://mlsanity.com/paper/2101.04096/) (2021)
- [SkinMap: Weighted Full-Body Skin Segmentation for Robust Remote Photoplethysmography](https://mlsanity.com/paper/2510.05296/) (2025)
- [Heart rate estimation in intense exercise videos](https://mlsanity.com/paper/2208.02509/) (2022)
- [Deep Learning Classification of Photoplethysmogram Signal for Hypertension Levels](https://mlsanity.com/paper/2405.14556/) (2024)
- [Facial Spatiotemporal Graphs: Leveraging the 3D Facial Surface for Remote Physiological Measurement](https://mlsanity.com/paper/2601.13724/) (2026)
- [Beyond Contact Sensors: Deep learning with Pseudo-Labeling for remote Photoplethysmography](https://mlsanity.com/paper/2609.10026/) (2026)

## Abstract (arXiv)

Deep learning based on Convolutional Neural Network (CNN) has shown promising results in various vision-based applications, recently also in camera-based vital signs monitoring. The CNN-based Photoplethysmography (PPG) extraction has, so far, been focused on performance rather than understanding. In this paper, we try to answer four questions with experiments aiming at improving our understanding of this methodology as it gains popularity. We conclude that the network exploits the blood absorption variation to extract the physiological signals, and that the choice and parameters (phase, spectral content, etc.) of the reference-signal may be more critical than anticipated. The availability of multiple convolutional kernels is necessary for CNN to arrive at a flexible channel combination through the spatial operation, but may not provide the same motion-robustness as a multi-site measurement using knowledge-based PPG extraction. Finally, we conclude that the PPG-related prior knowledge is still helpful for the CNN-based PPG extraction. Consequently, we recommend further investigation of hybrid CNN-based methods to include prior knowledge in their design.