  There has been growing interest in facial video-based remote
photoplethysmography (rPPG) measurement recently, with a focus on assessing
various vital signs such as heart rate and heart rate variability. Despite
previous efforts on static datasets, their approaches have been hindered by
inaccurate region of interest (ROI) localization and motion issues, and have
shown limited generalization in real-world scenarios. To address these
challenges, we propose a novel masked attention regularization (MAR-rPPG)
framework that mitigates the impact of ROI localization and complex motion
artifacts. Specifically, our approach first integrates a masked attention
regularization mechanism into the rPPG field to capture the visual semantic
consistency of facial clips, while it also employs a masking technique to
prevent the model from overfitting on inaccurate ROIs and subsequently
degrading its performance. Furthermore, we propose an enhanced rPPG expert
aggregation (EREA) network as the backbone to obtain rPPG signals and attention
maps simultaneously. Our EREA network is capable of discriminating divergent
attentions from different facial areas and retaining the consistency of
spatiotemporal attention maps. For motion robustness, a simple open source
detector MediaPipe for data preprocessing is sufficient for our framework due
to its superior capability of rPPG signal extraction and attention
regularization. Exhaustive experiments on three benchmark datasets (UBFC-rPPG,
PURE, and MMPD) substantiate the superiority of our proposed method,
outperforming recent state-of-the-art works by a considerable margin.
