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Temporal Gated Face Alignment Network for Camera-Based Physiological Sensing
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AI
DOI：10.1109/TCSS.2025.3559385.png)
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摘要
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The remote photoplethysmography (rPPG) technique estimates vital signs, such as heart rate (HR), by analyzing subtle skin color variation in facial videos induced by the pulse. However, it remains a critical challenge to robustly acquire cardiac pulse information in scenarios with head motion (e.g., rotation and swing), as it inevitably introduces interfering noise, such as facial geometric deformation and displacement. Most existing methods primarily focus on how to extract subtle pulse signals while neglecting the detrimental effects of noise, especially out-of-distribution motion patterns. In response to this, we propose a temporal gated face alignment network (TGFAN) to adaptively counteract motion noise in long-term video sequences. Specifically, the bidirectional temporal face alignment (BFA) block first captures interframe motion discrepancies to align the displaced face feature and then extracts motion-robust pulse features. Furthermore, we propose a learnable temporal gating mechanism that disentangles the features and dynamically guides motion-disturbed segments for feature alignment, thereby alleviating local head motion interference. Experimental evaluations on four benchmark datasets demonstrate our superior performance on both intradataset and cross-dataset tests.
Keyword:
Feature extraction
Faces
Videos
Spatiotemporal phenomena
Noise
Physiology
Estimation
Skin
Logic gates
Electronic mail
Feature alignment
heart rate (HR) estimation
remote photoplethysmography (rPPG)
temporal gated mechanism
期刊
IF:
4.9
论文数:
613
被引数:
6.8K
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引用论文
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Efficient Remote Photoplethysmography with Temporal Derivative Modules and Time-Shift Invariant Loss
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