SHINE-PPG: Non-Lambertian Intrinsic Decomposition for Illumination-Robust rPPG

Remote photoplethysmography (rPPG) enables non-contactphysiological monitoring by capturing subtle skin color variations inducedby cardiac cycles. Despite its promise, rPPG remains highly sensitive toenvironmental illumination. Existing illumination-aware methods sufferfrom two key limitations: (1) limited out-of-distribution (OOD) gen-eralization due to domain discrepancies between training and testingdata, and (2) reliance on the Lambertian assumption, which neglectsnon-Lambertian specular highlights that frequently corrupt facial skinsignals. In this paper, we propose SHINE-PPG (Specular-Highlight In-trinsic Network for rPPG Estimation), a novel framework that leveragesnon-Lambertian intrinsic decomposition to decouple facial videos intoillumination, reflectance, and specular components in a self-supervisedmanner. By isolating physiological information within the intrinsic re-flectance, our method effectively suppresses both ambient lighting vari-ations and surface highlights to recover high-fidelity rPPG signals. Tofurther enhance robustness, we introduce an adversarial illuminationenhancement strategy that dynamically synthesizes challenging unseenlighting conditions during training, significantly improving OOD general-ization. Extensive experiments on five benchmark datasets demonstratethat SHINE-PPG consistently outperforms previous methods, particularlyunder complex and dynamic illumination scenarios. The code is availableat https://github.com/Edmond-Yang/SHINE-PPG.