HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation
This addresses domain adaptation for non-contact physiological measurement, with incremental improvements in handling appearance variations.
The paper tackles performance degradation in remote photoplethysmography (rPPG) due to domain shifts by introducing frequency domain adaptation (FDA) and Harmonic-Constrained Optimal Transport (HOT), resulting in enhanced robustness and generalization across diverse datasets.
Remote photoplethysmography (rPPG) enables non-contact physiological measurement from facial videos; however, its practical deployment is often hindered by substantial performance degradation under domain shift. While recent deep learning-based rPPG methods have achieved strong performance on individual datasets, they frequently overfit to appearance-related factors, such as illumination, camera characteristics, and color response, that vary significantly across domains. To address this limitation, we introduce frequency domain adaptation (FDA) as a principled strategy for modeling appearance variation in rPPG. By transferring low-frequency spectral components that encode domain-dependent appearance characteristics, FDA encourages rPPG models to learn invariance to appearance variations while retaining cardiac-induced signals. To further support physiologically consistent alignment under such appearance variation, we propose Harmonic-Constrained Optimal Transport (HOT), which leverages the harmonic property of cardiac signals to guide alignment between original and FDA-transferred representations. Extensive cross-dataset experiments demonstrate that the proposed FDA and HOT framework effectively enhances the robustness and generalization of rPPG models across diverse datasets.