Spectral Quality-Based Weighted Multi-ROI Fusion for Robust Imaging Photoplethysmography

Abstract

Imaging photoplethysmography (iPPG) enables non-contact heart rate (HR) estimation from facial videos, yet its reliability is strongly affected by spatial and temporal signal variability. While many studies focus on improving signal extraction algorithms, the impact of explicit facial region selection and quality-aware fusion remains underexplored. This study systematically proposed a spectral quality-based weighted multi-ROI fusion method and investigates the role of facial region-of-interest (ROI) selection, and temporal quality gating within a controlled and interpretable iPPG framework. A classical pipeline is employed to isolate spatial and quality-related effects. Experiments were conducted on 41 subjects from the UBFC-rPPG dataset. The results demonstrate that quality-weighted multi-ROI fusion provides statistically significant and more consistent performance compared to single-ROI and simple averaging strategies. With moderate temporal gating (τ = 0.20), the proposed method improves estimation accuracy while maintaining high coverage (92.31%) and full subject inclusion. A stricter threshold (τ = 0.25) further reduces the mean absolute error to 2.89 bpm and increases correlation to 0.86, albeit with reduced window-level coverage (41%). These findings highlight a clear trade-off between numerical accuracy and data availability and emphasize the importance of jointly considering spatial reliability and temporal quality control for robust and interpretable iPPG-based heart rate monitoring in realistic real-world deployment scenarios.