无需源数据即可实现面部视频生理信号跨域测量，提升模型泛化能力。
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
- 提出三分支时空一致性网络，增强跨域特征稳定性
- 设计频域沃尔多斯特距离损失，有效对齐不同域的功率谱分布
- 首个无源域自适应rPPG基准，适合隐私敏感场景应用
远程光电容积脉搏波（rPPG）是一种通过面部视频非接触式检测血容量变化的方法，用于生理参数测量。传统rPPG模型在未见领域中泛化能力差。现有解决方案依赖领域泛化（DG）或领域自适应（DA），但需访问源域和目标域数据，在源数据受限或涉及隐私时无法使用。本文首次提出rPPG领域的无源域自适应基准（SFDA-rPPG），可在不访问源域数据的情况下实现有效域自适应。所提框架采用三分支时空一致性网络（TSTC-Net）提升跨域特征一致性，并提出基于频域沃尔多斯特距离（FWD）的新分布对齐损失，利用最优传输对齐各域的功率谱分布，进一步强化三分支对齐。大量跨域实验与消融研究验证了方法的有效性。结果表明，所提FWD损失对分布对齐贡献显著，为未来研究与应用提供重要参考。代码已开源：https://github.com/XieYiping66/SFDA-rPPG
原文摘要 · Abstract (English)
Remote Photoplethysmography (rPPG) is a non-contact method that uses facial video to predict changes in blood volume, enabling physiological metrics measurement. Traditional rPPG models often struggle with poor generalization capacity in unseen domains. Current solutions to this problem is to improve its generalization in the target domain through Domain Generalization (DG) or Domain Adaptation (DA). However, both traditional methods require access to both source domain data and target domain data, which cannot be implemented in scenarios with limited access to source data, and another issue is the privacy of accessing source domain data. In this paper, we propose the first Source-free Domain Adaptation benchmark for rPPG measurement (SFDA-rPPG), which overcomes these limitations by enabling effective domain adaptation without access to source domain data. Our framework incorporates a Three-Branch Spatio-Temporal Consistency Network (TSTC-Net) to enhance feature consistency across domains. Furthermore, we propose a new rPPG distribution alignment loss based on the Frequency-domain Wasserstein Distance (FWD), which leverages optimal transport to align power spectrum distributions across domains effectively and further enforces the alignment of the three branches. Extensive cross-domain experiments and ablation studies demonstrate the effectiveness of our proposed method in source-free domain adaptation settings. Our findings highlight the significant contribution of the proposed FWD loss for distributional alignment, providing a valuable reference for future research and applications. The source code is available at https://github.com/XieYiping66/SFDA-rPPG
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