Deep learning-based image enhancement for robust remote photoplethysmography in various illumination scenarios
Shutao Chen, Sui Kei Ho, Jing Wei Chin, Kin Ho Luo, Tsz Tai Chan, Richard H. Y. So, Kwan Long Wong
Abstract
Remote photoplethysmography (rPPG) is a non-invasive and convenient approach for measuring human vital signs using a camera. However, accurate measurement can be challenging due to the different illumination of the surrounding environment. In this study, we present a deep learning-based image enhancement model (IEM) inspired by the Retinex theory to improve the robustness of rPPG signal extraction and heart rate (HR) estimation in different lighting conditions. We fine-tuned the IEM with a time-shifted negative Pearson correlation between the PPG signal ground truth and the predicted rPPG signal from a pre-trained 3D CNN (PhysNet). We evaluated our method using a publicly available dataset (BH-rPPG) of various lighting scenarios and our own private dataset. Our results demonstrate that our proposed model is generalizable and significantly improves rPPG extraction and HR estimation accuracies across a range of illumination intensities.