HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation

Abstract

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.

Figures (8)

• Figure 1: Comparison with existing rPPG methods. Conventional approaches are trained on labeled source-domain data and directly deployed to the target domain, resulting in performance degradation under domain shift. In contrast, ours leverages unlabeled target-domain samples to synthesize source data with target-domain appearance during training, encouraging appearance-invariant learning and improving cross-domain generalization.
• Figure 2: Overall architecture of HOT.
• Figure 3: Illustration of FDA: low-frequency amplitude replacement in the Fourier domain while preserving the phase spectrum.
• Figure 4: Cross-dataset evaluation on UBFC-rPPG $\rightarrow$ MMPD. From left to right: Bland--Altman plot of PhysNet + HOT, Bland--Altman plot of PhysNet, scatter plot of PhysNet + HOT, and scatter plot of PhysNet. The dashed horizontal lines in the Bland--Altman plots denote the 95% limits of agreement, while the dashed diagonal lines in the scatter plots denote the identity line.
• Figure 5: Effect of the low-frequency replacement ratio $\beta$ on cross-dataset performance.