Reliability-Aware Weighted Multi-Scale Spatio-Temporal Maps for Heart Rate Monitoring
For researchers in remote photoplethysmography, this work improves robustness to motion and illumination, but the gains are incremental over existing SSL methods.
The paper addresses the problem of heart rate estimation from facial videos under unconstrained conditions by proposing a Reliability-Aware Weighted Multi-Scale Spatio-Temporal map and a contrastive learning approach. The method achieves lower heart rate estimation error and higher Pearson correlation than existing SSL-based rPPG methods on public benchmarks.
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 approach based on Swin-Unet, where positive pairs are generated from conventional rPPG signals and temporally expanded WMST maps. Moreover, we introduce a new High-High-High (HHH) wavelet map as a negative example that maintains motion and structural details while filtering out physiological information. Here, our aim is to estimate heart rate (HR), and the experiments on public rPPG benchmarks show that our approach enhances motion and illumination robustness with lower HR estimation error and higher Pearson correlation than existing Self-Supervised Learning (SSL) based rPPG methods.