UDA-rPPG: Unsupervised Geometric-Physiological Domain Anchoring for Low-Light rPPG Measurement

Remote photoplethysmography (rPPG) is a critical technique for non-contact monitoring of human vital signs using facial video data. Most of the existing rPPG approaches, either supervised ones relying on ground-truth physiological signals or less constrained unsupervised ones, primarily address the problem of inaccurate physiological measurements under normal lighting conditions. However, few works focus on handling physiological measurements in extremely low-light scenarios. To this end, we propose an unsupervised geometric-physiological domain anchoring for low-light rPPG measurement (UDA-rPPG). Firstly, we develop a geometric anchoring video enhancement module (GAEM) that can enhance video brightness while preserving rPPG signals, achieving accurate geometric-domain face anchoring. Secondly, we introduce a low-light stable spatial-temporal network (LS-Phys), which focuses on high-frequency information to mitigate noise in low-light scenarios. Finally, a novel highest-peak priority learning strategy is presented to learn physiological-domain rPPG signal anchoring by emphasizing peak information, which enhances the robustness of rPPG measurements in low-light environments. Additionally, we construct a comprehensive low-light rPPG dataset (LRPD) that contains both visible and near-infrared videos under low-light scenarios. Extensive experiments demonstrate the superior performance of our approach over state-of-the-art unsupervised rPPG methods in different light conditions and verify the generalization of UDA-rPPG on cross-dataset testing. Our code and dataset are available at https://github.com/wwenmaositu/LS-rPPG-LRPD.