# U-FaceBP: Uncertainty-aware Bayesian Ensemble Deep Learning for Face Video-based Blood Pressure Estimation

arXiv: https://arxiv.org/abs/2412.10679 · HTML: https://mlsanity.com/paper/2412.10679/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 84 references that are in the map.
- First posted on arXiv 14 Dec 2024.

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

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- [Full-Body Cardiovascular Sensing with Remote Photoplethysmography](https://mlsanity.com/paper/2303.09638/) (2023)
- [CalibrationPhys: Self-supervised Video-based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras](https://mlsanity.com/paper/2310.15043/) (2023)
- [MSPM: A Multi-Site Physiological Monitoring Dataset for Remote Pulse, Respiration, and Blood Pressure Estimation](https://mlsanity.com/paper/2402.02224/) (2024)
- [Exploring Fusion Techniques in Multimodal AI-Based Recruitment: Insights from FairCVdb](https://mlsanity.com/paper/2407.16892/) (2024)

## Abstract (arXiv)

Blood pressure (BP) measurement is crucial for daily health assessment. Remote photoplethysmography (rPPG), which extracts pulse waves from face videos captured by a camera, has the potential to enable convenient BP measurement without specialized medical devices. However, there are various uncertainties in BP estimation using rPPG, leading to limited estimation performance and reliability. In this paper, we propose U-FaceBP, an uncertainty-aware Bayesian ensemble deep learning method for face video-based BP estimation. U-FaceBP models aleatoric and epistemic uncertainties in face video-based BP estimation with a Bayesian neural network (BNN). Additionally, we design U-FaceBP as an ensemble method, estimating BP from rPPG signals, PPG signals derived from face videos, and face images using multiple BNNs. Large-scale experiments on two datasets involving 1197 subjects from diverse racial groups demonstrate that U-FaceBP outperforms state-of-the-art BP estimation methods. Furthermore, we show that the uncertainty estimates provided by U-FaceBP are informative and useful for guiding modality fusion, assessing prediction reliability, and analyzing performance across racial groups.