KDPPG: Prior-Guided Knowledge Distillation for Robust Streaming rPPG Estimation

Remote photoplethysmography (rPPG) enables contactless physiological monitoring, but robust low-latency deployment remains challenging. High-performance methods typically rely on buffered video clips, whereas streaming-compatible models often provide weaker temporal modeling and robustness. We propose KDPPG, a prior-guided knowledge distillation framework that transfers spatial and geometric knowledge from a clip-wise teacher to a causal student. KDPPG-T uses facial masks and projected normalized coordinate code (PNCC) in a dual-prior refinement module to suppress background illumination artifacts and improve robustness to motion. KDPPG-S processes one frame per time step, maintains a recurrent state, and incorporates a parallel harmonic resonance module (PHRM) to model periodic dynamics without fixed clip buffering or inference-time priors. Extensive experiments across multiple datasets demonstrate that KDPPG achieves state-of-the-art accuracy, while KDPPG-S offers high inference efficiency and low computational cost, making it suitable for practical streaming deployment.