Illumination-Invariant Normalization for Robust rPPG Extraction

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within ±6–12% with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions.