  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.
