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Remote Photoplethysmography (rPPG) has been attracting increasing attention due to its potential in a wide range of application scenarios such as physical training, clinical monitoring, and face anti-spoofing. On top of conventional solutions, deep-learning approach starts to dominate in rPPG estimation and achieves top-level performance. However, most of them try to integrate preprocessing steps such as the ROI selection into an end-to-end network, which may diverge the attention and also limit the generalization in other scenarios with different input skin regions. In this work, we focus on learning the intrinsic rPPG feature and design a lightweight but effective rPPG estimation network based on spatiotemporal convolution. To further improve the robustness, on top of the basic design we propose the Noise-Disentangled DeeprPPG (ND-DeeprPPG) by disentangling the environmental noise from the raw rPPG feature with an adversarial canonical correlation analysis learning strategy. Background regions are employed as a reference to guide the noise disentangling in a self-supervised manner. Extensive experiments show that our ND-DeeprPPG not only outperforms the state-of-the-arts on heart rate estimation but also exhibits promising robustness in cross-skin-region, cross-dataset scenarios and other rPPG-based tasks.
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