PhysLight: Accurate rPPG Heart Rate Measurement with Adaptive Video Relighting

Menglin Zhang, Xiaoxin Guo, Bohao Qu, Xiaofeng Cao, Shuifa Sun, Qing Guo

Abstract: Facial video-based remote physiological measurement (rPPG) can non-invasively estimate vital signs, such as heart rate (HR), which often faces challenges under varying lighting conditions. We propose the PhysLight framework to enhance the accuracy of rPPG heart rate measurement through adaptive video relighting. Our approach subtly modifies illumination in video frames to improve detection accuracy while maintaining visual quality. The framework includes a GenLightNet to extract ideal lighting priors and a WipeLightNet module to refine poorly lit videos. Extensive evaluations on benchmark datasets show that our method significantly improves HR estimation reliability, outperforming existing baselines and enhancing non-contact physiological monitoring in diverse environments.

External IDs:dblp:conf/icmcs/ZhangGQCSG25