December 5, 2025MathematicsOpen Access

A Dual-Branch Spatio-Temporal Feature Differencing Method for Robust rPPG Estimation

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Dual-branch framework improves rPPG estimation under noise conditions, indicating resilience to motion blur and environmental changes.

Estimation accuracy improves in noisy conditions, particularly with a dual-branch approach.
Error reduction observed in various noise settings with physiological signals assessed from rPPG.
Deep learning framework employs spatio-temporal models to address motion blur and illumination issues.
Findings indicate potential for robust performance in real-world scenarios with significant noise.

Cho et al. (2025) studied this question.

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December 5, 2025MathematicsOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Dual-branch framework improves rPPG estimation under noise conditions, indicating resilience to motion blur and environmental changes.

Estimation accuracy improves in noisy conditions, particularly with a dual-branch approach.
Error reduction observed in various noise settings with physiological signals assessed from rPPG.
Deep learning framework employs spatio-temporal models to address motion blur and illumination issues.
Findings indicate potential for robust performance in real-world scenarios with significant noise.

Cho et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark