  We introduce Hyper-Skin, a hyperspectral dataset covering wide range of
wavelengths from visible (VIS) spectrum (400nm - 700nm) to near-infrared (NIR)
spectrum (700nm - 1000nm), uniquely designed to facilitate research on facial
skin-spectra reconstruction. By reconstructing skin spectra from RGB images,
our dataset enables the study of hyperspectral skin analysis, such as melanin
and hemoglobin concentrations, directly on the consumer device. Overcoming
limitations of existing datasets, Hyper-Skin consists of diverse facial skin
data collected with a pushbroom hyperspectral camera. With 330 hyperspectral
cubes from 51 subjects, the dataset covers the facial skin from different
angles and facial poses. Each hyperspectral cube has dimensions of
1024$\times$1024$\times$448, resulting in millions of spectra vectors per
image. The dataset, carefully curated in adherence to ethical guidelines,
includes paired hyperspectral images and synthetic RGB images generated using
real camera responses. We demonstrate the efficacy of our dataset by showcasing
skin spectra reconstruction using state-of-the-art models on 31 bands of
hyperspectral data resampled in the VIS and NIR spectrum. This Hyper-Skin
dataset would be a valuable resource to NeurIPS community, encouraging the
development of novel algorithms for skin spectral reconstruction while
fostering interdisciplinary collaboration in hyperspectral skin analysis
related to cosmetology and skin's well-being. Instructions to request the data
and the related benchmarking codes are publicly available at:
\url{https://github.com/hyperspectral-skin/Hyper-Skin-2023}.
