Camera-Based Remote Physiology Sensing for Hundreds of Subjects Across Skin Tones
This work addresses the need for diverse and consistent datasets in remote physiology sensing, particularly for evaluating performance across different skin tones, but it is incremental as it builds on existing methods with a new dataset.
The paper tackled the problem of limited size and diversity in remote photoplethysmography (rPPG) datasets by analyzing the VitalVideo dataset, which includes 893 subjects across 6 skin tones, and found that datasets with 300 to 700 subjects are sufficient for effective model training.
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.