Advancing Generalizable Remote Physiological Measurement through the
  Integration of Explicit and Implicit Prior Knowledge
Yuting Zhang Hao Lu Xin Liu Yingcong Chen Kaishun Wu
Remote photoplethysmography (rPPG) is a promising technology that captures
physiological signals from face videos, with potential applications in medical
health, emotional computing, and biosecurity recognition. The demand for rPPG
tasks has expanded from demonstrating good performance on intra-dataset testing
to cross-dataset testing (i.e., domain generalization). However, most existing
methods have overlooked the prior knowledge of rPPG, resulting in poor
generalization ability. In this paper, we propose a novel framework that
simultaneously utilizes explicit and implicit prior knowledge in the rPPG task.
Specifically, we systematically analyze the causes of noise sources (e.g.,
different camera, lighting, skin types, and movement) across different domains
and incorporate these prior knowledge into the network. Additionally, we
leverage a two-branch network to disentangle the physiological feature
distribution from noises through implicit label correlation. Our extensive
experiments demonstrate that the proposed method not only outperforms
state-of-the-art methods on RGB cross-dataset evaluation but also generalizes
well from RGB datasets to NIR datasets. The code is available at
https://github.com/keke-nice/Greip.