Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones
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arXiv
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| Auteurs principaux: | , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916197445926912 |
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| author | Tang, Jiankai Li, Xinyi Liu, Jiacheng Zhang, Xiyuxing Wang, Zeyu Wang, Yuntao |
| author_facet | Tang, Jiankai Li, Xinyi Liu, Jiacheng Zhang, Xiyuxing Wang, Zeyu Wang, Yuntao |
| contents | Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras. Despite advancements, existing datasets fall short in terms of size and diversity, limiting comprehensive evaluation under diverse conditions. This paper presents an in-depth analysis of the VitalVideo dataset, the largest real-world rPPG dataset to date, encompassing 893 subjects and 6 Fitzpatrick skin tones. Our experimentation with six unsupervised methods and three supervised models demonstrates that datasets comprising a few hundred subjects(i.e., 300 for UBFC-rPPG, 500 for PURE, and 700 for MMPD-Simple) are sufficient for effective rPPG model training. Our findings highlight the importance of diversity and consistency in skin tones for precise performance evaluation across different datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05003 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones Tang, Jiankai Li, Xinyi Liu, Jiacheng Zhang, Xiyuxing Wang, Zeyu Wang, Yuntao Computer Vision and Pattern Recognition Artificial Intelligence Remote photoplethysmography (rPPG) emerges as a promising method for non-invasive, convenient measurement of vital signs, utilizing the widespread presence of cameras. Despite advancements, existing datasets fall short in terms of size and diversity, limiting comprehensive evaluation under diverse conditions. This paper presents an in-depth analysis of the VitalVideo dataset, the largest real-world rPPG dataset to date, encompassing 893 subjects and 6 Fitzpatrick skin tones. Our experimentation with six unsupervised methods and three supervised models demonstrates that datasets comprising a few hundred subjects(i.e., 300 for UBFC-rPPG, 500 for PURE, and 700 for MMPD-Simple) are sufficient for effective rPPG model training. Our findings highlight the importance of diversity and consistency in skin tones for precise performance evaluation across different datasets. |
| title | Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2404.05003 |