Remote photoplethysmography (rPPG) technology has drawn increasing attention in recent years. It can extract Blood Volume Pulse (BVP) from facial videos, making many applications like health monitoring and emotional analysis more accessible. However, as the BVP signal is easily affected by environmental changes, existing methods struggle to generalize well for unseen domains. In this paper, we systematically address the domain shift problem in the rPPG measurement task. We show that most domain generalization methods do not work well in this problem, as domain labels are ambiguous in complicated environmental changes. In light of this, we propose a domain-label-free approach called NEuron STructure modeling (NEST). NEST improves the generalization capacity by maximizing the coverage of feature space during training, which reduces the chance for under-optimized feature activation during inference. Besides, NEST can also enrich and enhance domain invariant features across multi-domain. We create and benchmark a large-scale domain generalization protocol for the rPPG measurement task. Extensive experiments show that our approach outperforms the state-of-the-art methods on both cross-dataset and intra-dataset settings.

翻译：远程光电容积描记（rPPG）技术近年来受到越来越多的关注。该技术能够从面部视频中提取血容量脉搏（BVP）信号，从而使得健康监测和情绪分析等应用更加便捷。然而，由于BVP信号易受环境变化影响，现有方法在未知领域上的泛化能力往往不足。本文系统性地研究了rPPG测量任务中的领域偏移问题。我们指出，大多数领域泛化方法在此问题中效果不佳，因为领域标签在复杂的环境变化中具有模糊性。为此，我们提出一种无需领域标签的方法——神经元结构建模（NEST）。NEST通过在训练过程中最大化特征空间的覆盖范围来提升泛化能力，从而减少推理过程中特征激活不充分的情况。此外，NEST还能丰富并增强跨领域的域不变特征。我们构建并评测了一个用于rPPG测量任务的大规模领域泛化基准协议。大量实验表明，我们的方法在跨数据集和数据集内设置下均优于当前最先进的方法。