  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.
