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arXiv · 2604.11395

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

Gan Pei·Junhao Ning·Boqiu Shen·Yan Zhu·Menghan Hu
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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.

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Gan Pei, Junhao Ning, Boqiu Shen, Yan Zhu, Menghan Hu. 2026-04-13. Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising. https://arxiv.org/abs/2604.11395

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