Remote PPG Measurement Using a Synergistic Time-Frequency Network

Yiming Li

Abstract: Remote photoplethysmography (rPPG) aims to estimate the blood volume pulse (BVP) signal from facial videos. Existing rPPG approaches still suffer from limitations. We attribute this issue to two primary problems: (1) the reliance solely on time-domain processing, which makes the signal susceptible to interference, and (2) the presence of a phase discrepancy between the supervision signal and the ground-truth PPG. To address these problems, we propose TFSNet, a novel time-frequency synergy network for rPPG signal estimation and heart rate prediction. Specifically, we leverage time-frequency fusion (TFF) module, which integrates frequency-domain information into the learning process to enrich time-domain representations. Additionally, we introduce the amplitude-phase decoupling (APD) module, which apply phase compensation in frequency domain to mitigate the adverse effects of incorrect phase supervision. Extensive experiments demonstrate that TFSNet achieves state-of-the-art performance, significantly outperforming current approaches in both accuracy and robustness.