LQ-rPPG introduces label quantization and coarse-to-fine hierarchical supervision to mitigate noise in contact PPG labels for improved remote photoplethysmography estimation.
A Reproducible Study on Remote Heart Rate Measurement
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abstract
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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cs.CV 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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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.