akramic/mcd_rppg
Permissive or share-alike
The declared licence (cc-by-4.0) is classified as permissive.
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- Declared licence: cc-by-4.0 (from the tag)
- Licence files: no licence file at the top level of the repository
- Access gated on Hugging Face: no
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Citation text from the card
@inproceedings{10.1145/3746027.3758255,
author = {Egorov, Konstantin and Botman, Stepan and Blinov, Pavel and Zubkova, Galina and Ivaschenko, Anton and Kolsanov, Alexander and Savchenko, Andrey},
title = {Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation},
year = {2025},
isbn = {9798400720352},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3746027.3758255},
doi = {10.1145/3746027.3758255},
abstract = {Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical issues of existing publicly available datasets: small size, privacy concerns with facial videos, and lack of diversity in conditions. The paper introduces a novel, comprehensive, large-scale multi-view video dataset for rPPG and health biomarkers estimation. Our dataset comprises 3600 synchronized video recordings from 600 subjects, captured under varied conditions (resting and post-exercise) using multiple consumer-grade cameras at different angles. To enable multimodal analysis of physiological states, each recording is paired with a 100 Hz PPG signal and extended health metrics, such as electrocardiogram, arterial blood pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and stress level. Using this data, we train an efficient rPPG model and compare its quality with existing approaches in cross-dataset scenarios. The public release of our dataset and model should significantly s
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