Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising

TL;DR

This paper introduces two modules that improve remote photoplethysmography (rPPG) for non-contact heart rate measurement from facial videos, especially under motion, by optimizing ROI and denoising signals with graph processing.

Contribution

It proposes angle-guided ROI optimization and graph signal denoising modules that enhance rPPG accuracy during facial motions, compatible with existing reflection model-based methods.

Findings

Reduced MAE by an average of 20.38% over baseline.

Modules effectively suppress motion artifacts in rPPG signals.

Validated on three public datasets with improved performance.

Abstract

Remote photoplethysmography (rPPG) enables non-contact heart rate measurement from facial videos, but its performance is significantly degraded by facial motions such as speaking and head shaking. To address this issue, we propose two plug-and-play modules. The Angle-guided ROI Adaptive Optimization module quantifies ROI-Camera angles to refine motion-affected signals and capture global motion, while the Multi-region Joint Graph Signal Denoising module jointly models intra- and inter-regional ROI signals using graph signal processing to suppress motion artifacts. The modules are compatible with reflection model-based rPPG methods and validated on three public datasets. Results show that jointly use markedly reduces MAE, with an average decrease of 20.38\% over the baseline, while ablation studies confirm the effectiveness of each module. The work demonstrates the potential of angle-guided optimization and graph-based denoising to enhance rPPG performance in motion scenarios.