Reliability-Aware Weighted Multi-Scale Spatio-Temporal Maps for Heart Rate Monitoring
Remote photoplethysmography (rPPG) allows for the contactless estimation of physiological signals from facial videos by analyzing subtle skin color changes. However, rPPG signals are extremely susceptible to illumination changes, motion, shadows, and specular reflections, resulting in low-quality signals in unconstrained environments. To overcome these issues, we present a Reliability-Aware Weighted Multi-Scale Spatio-Temporal (WMST) map that models pixel reliability through the suppression of environmental noises. These noises are modeled using different weighting strategies to focus on more physiologically valid areas. Leveraging the WMST map, we develop an SSL contrastive learning appr...
| Authors | Arpan Bairagi, Rakesh Dey, Siladittya Manna et al. |
|---|---|
| Primary category | eess.IV |
| Published | 27 March 2026 |
| Last updated at source | 27 March 2026 |
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