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.

翻译：远程光电容积描记术（rPPG）是一种通过面部视频捕捉生理信号的前沿技术，在医疗健康、情感计算和生物安全识别等领域具有广阔应用前景。当前rPPG任务的需求已从实现数据集内测试的良好性能扩展到跨数据集测试（即领域泛化）。然而，现有方法大多忽视了rPPG任务的先验知识，导致泛化能力不足。本文提出一种创新框架，在rPPG任务中同时利用显式与隐式先验知识。具体而言，我们系统分析了跨领域噪声源（如不同摄像机、光照条件、皮肤类型和运动状态）的成因，并将这些先验知识融入网络架构。此外，我们通过双分支网络利用隐式标签关联性，实现生理特征分布与噪声的解耦分离。大量实验表明，所提方法不仅在RGB跨数据集评估中超越现有最优方法，还能从RGB数据集有效泛化至近红外数据集。代码已开源：https://github.com/keke-nice/Greip。