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
this https URL

本文提出了一种利用远程光电容积图术（rPPG）任务中的显性和隐性先验知识的新框架，通过系统分析不同领域的噪声源，并将这些先验知识纳入网络进行训练，同时利用两个分支网络通过隐式标签相关性去除噪声并提取生理特征分布，我们的实验证明，该方法不仅在 RGB 跨数据集评估上胜过现有方法，而且能够很好地从 RGB 数据集泛化到 NIR 数据集。