Feature-Disentangling RGB-NIR Fusion Network for Remote Driver Physiological Measurement

Remote photoplethysmography (rPPG) is a crucial technique for non-contact heart rate (HR) estimation using facial videos, gaining significance in driver monitoring systems where contact-based measurements are impractical. Existing rPPG methods often rely on either RGB or NIR data, each susceptible to limitations under motion artifacts and varying illumination in real-world driving scenarios. To address these challenges, we introduce a novel RGB-NIR fusion model tailored for robust rPPG and HR estimation in dynamic vehicle environments. Our approach features two main contributions, an NIR-specific decoder that facilitates effective cross-modal knowledge transfer from RGB to NIR, enhancing model adaptability, and a dual autoencoder architecture for efficient feature disentanglement and reconstruction, mitigating noise from driver motion and changing lighting conditions. Comprehensive evaluations, including inter- and cross-dataset testing and ablation studies across various driving and garage conditions, demonstrate that our model achieves superior performance on the MR-NIRP car dataset, showcasing significant robustness in complex vehicular environments.