A Reproducible Study on Remote Heart Rate Measurement
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This paper studies the problem of reproducible research in remote photoplethysmography (rPPG). Most of the work published in this domain is assessed on privately-owned databases, making it difficult to evaluate proposed algorithms in a standard and principled manner. As a consequence, we present a new, publicly available database containing a relatively large number of subjects recorded under two different lighting conditions. Also, three state-of-the-art rPPG algorithms from the literature were selected, implemented and released as open source free software. After a thorough, unbiased experimental evaluation in various settings, it is shown that none of the selected algorithms is precise enough to be used in a real-world scenario.
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Cited by 3 Pith papers
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StreamPPG: Low-Latency rPPG Estimation via Consistent Privileged Learning
StreamPPG enables low-latency frame-wise rPPG estimation via consistent privileged learning, achieving SOTA accuracy on multiple datasets with real-time edge-device throughput.
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A Skin-Tone-Aware Dual-Representation Remote Photoplethysmography Framework for Contactless Respiratory Rate Estimation
Skin-tone-aware Eulerian-Lagrangian rPPG framework with contrastive loss and new RR-rPPG dataset reduces MAE by up to 42.1% for contactless respiratory rate estimation on two datasets.
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LQ-rPPG: A Label-Quantized Coarse-to-Fine Learning Framework for Remote Physiological Measurement
LQ-rPPG introduces label quantization and coarse-to-fine hierarchical supervision to mitigate noise in contact PPG labels for improved remote photoplethysmography estimation.
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