Exploiting Near-Infrared Priors for Illumination-Robust rPPG Estimation

Remote photoplethysmography (rPPG) enables contact-free physiological measurement from facial videos, but remains vulnerable to illumination variations that can easily overwhelm subtle pulse-related appearance changes. RGB-NIR sensing provides a promising solution by introducing near-infrared observations that are relatively more stable than visible-light imaging. However, raw RGB-NIR features still contain illumination-related disturbance, and direct fusion may propagate it into the final prediction. To address this problem, we propose the Near-Infrared Prior Network (NIRP-Net), a prior-guided framework for illumination-robust RGB-NIR rPPG estimation. NIRP-Net first performs prior-guided feature disentanglement to derive disturbance-suppressed RGB and NIR physiological representations by exploiting the 940-nm low-irradiance and physiology-irrelevant background priors. It then introduces gated cross-attention aggregation to selectively retrieve and aggregate complementary NIR cues for temporal prediction. We further collect FaceIVL, a new RGB-NIR facial rPPG dataset under controlled illumination variations. Experiments on public datasets and FaceIVL demonstrate state-of-the-art accuracy and greater computational efficiency than existing RGB-NIR approaches.