Anchoring Physiological Invariance, Expanding Domain Plasticity: Prior-Stabilized Dynamic Adaptation for Continual rPPG Measurement

Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos, but performance often degrades under unseen environments due to severe domain shifts. Continual learning offers a practical paradigm for updating rPPG models over sequential domains. However, it is challenged by two coupled issues: First, adapting to new domains may overwrite previously acquired physiological representations, resulting in the degradation of physiological consistency across domains. Second, drastic environmental variations induce heterogeneous facial video distributions, making the adaptation process unstable and prone to suboptimal convergence. To address these challenges, we propose ApexPhys, a continual rPPG framework that simultaneously anchors physiological invariance and expands domain plasticity. Specifically, we introduce a Backbone Consistency Representation Preservation (BCRP) mechanism, which leverages the Fisher Information Matrix to identify and preserve parameters critical to physiological consistency. To enhance adaptation flexibility under diverse environmental shifts, we further propose a Hierarchical Expansion Strategy (HES) that autonomously perceives layer-wise distribution discrepancies and dynamically expands hierarchical adaptation branches. Additionally, we develop a Prior-Guided Pathfinding (PGP) strategy, which leverages parameter inheritance to guide the adaptation along a stable trajectory. Experiments on five public datasets demonstrate that ApexPhys achieves strong anti-forgetting performance and robust adaptation under continual learning.