Advancing Generalizable Remote Physiological Measurement Through the Integration of Explicit and Implicit Prior Knowledge

Remote photoplethysmography (rPPG) is a promising technology for capturing physiological signals from facial videos, with potential applications in medical health, affective computing, and biometric recognition. The demand for rPPG tasks has evolved from achieving high performance in intra-dataset testing to excelling in cross-dataset testing (i.e., domain generalization). However, most existing methods have overlooked the incorporation of prior knowledge specific to rPPG, leading to limited generalization capabilities. In this paper, we propose a novel framework that effectively integrates both explicit and implicit prior knowledge into the rPPG task. Specifically, we conduct a systematic analysis of noise sources (e.g., variations in cameras, lighting conditions, skin types, and motion) across different domains and embed this prior knowledge into the network design. Furthermore, we employ a two-branch network to disentangle physiological feature distributions from noise through implicit label correlation. Extensive experiments demonstrate that the proposed method not only surpasses state-of-the-art approaches in RGB cross-dataset evaluation but also exhibits strong generalization from RGB datasets to NIR datasets. The code is publicly available at https://github.com/keke-nice/Greip.