遠程光體積變化描記圖提供了一種非接觸式方法，用於監測來自面部影像的生理信號。現有的基於學習的方法雖然在數據集內測試的情況下相當有效，但在跨數據集測試上效能嚴重退化。在本論文中，我們將跨數據集測試作為領域泛化問題來解決，並提出了一種新穎的領域泛化遠程心率估計網路來學習領域泛化遠程心率估計器。為此，我們開發了一個特徵分離學習框架，以從輸入面部影像中分離 遠程光體積變化描記圖、身份和領域特徵。接著，我們提出了一種領域置換策略，以進一步約束解開的遠程光體積變化描記圖特徵對不同領域保持不變。最後，我們設計了一種新的對抗領域增強策略來擴大領域泛化遠程心率估計網路的域範圍。我們的實驗結果表明，在 UBFC-rPPG、PURE、COHFACE和 VIPL-HR數據集的多個跨領域測試設置中，我們所提出的領域泛化遠程心率估計網路皆優於其他遠程心率估計方法。

Remote photoplethysmography (rPPG) offers a contactless method for monitoring physiological signals from facial videos. Existing learning-based methods, although work effectively on intra-dataset scenarios, degrade severely on cross-dataset testing. In this thesis, we address the cross-dataset testing as a domain generalization problem and propose a novel DG-rPPGNet to learn a domain generalized rPPG estimator. To this end, we develop a feature disentangled learning framework to disentangle rPPG, identity, and domain features from input facial videos. Next, we propose a domain permutation strategy to further constrain the disentangled rPPG features to be invariant to different domains. Finally, we design a novel adversarial domain augmentation strategy to enlarge the domain sphere of DG-rPPGNet. Our experimental results show that DG-rPPGNet outperforms other rPPG estimation methods in many cross-domain settings on UBFC-rPPG, PURE, COHFACE, and VIPL-HR datasets.

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