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

• URL: http://arxiv.org/abs/2604.01675v1
• Date: Thu, 02 Apr 2026 06:24:06 GMT
• Title: HOT: Harmonic-Constrained Optimal Transport for Remote Photoplethysmography Domain Adaptation
• Abstract summary: Remote photoplethysmography (rmography) enables non-contact measurement from facial videos.<n>Recent deep learning-based FDA methods have achieved strong performance on individual datasets.<n>We introduce frequency domain adaptation (FDA) as a principled strategy for modeling appearance variation in rvariance.
• Score: 2.5608506499175103
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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