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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.11395 |
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| _version_ | 1866911588770906112 |
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| author | Pei, Gan Ning, Junhao Shen, Boqiu Zhu, Yan Hu, Menghan |
| author_facet | Pei, Gan Ning, Junhao Shen, Boqiu Zhu, Yan Hu, Menghan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_11395 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising Pei, Gan Ning, Junhao Shen, Boqiu Zhu, Yan Hu, Menghan Computer Vision and Pattern Recognition 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. |
| title | Video-based Heart Rate Estimation with Angle-guided ROI Optimization and Graph Signal Denoising |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.11395 |