# Hyper-Skin: A Hyperspectral Dataset for Reconstructing Facial Skin-Spectra from RGB Images

arXiv: https://arxiv.org/abs/2310.17911 · HTML: https://mlsanity.com/paper/2310.17911/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2023.

## Built on by (most cited first, 7 of 7)

- [Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography](https://mlsanity.com/paper/2407.17996/) (2024)
- [Hyperspectral Imaging](https://mlsanity.com/paper/2508.08107/) (2025)
- [Hyperspectral Reconstruction of Skin Through Fusion of Scattering Transform Features](https://mlsanity.com/paper/2404.10030/) (2024)
- [From Image- to Pixel-level: Label-efficient Hyperspectral Image Reconstruction](https://mlsanity.com/paper/2503.06852/) (2025)
- [PHASE: Physiology-Aware Hyperspectral Reconstruction via Object-to-Human Domain Adaptation](https://mlsanity.com/paper/2511.13020/) (2025)
- [FUN: A Focal U-Net Combining Reconstruction and Object Detection for Snapshot Spectral Imaging](https://mlsanity.com/paper/2604.27653/) (2026)
- [Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment](https://mlsanity.com/paper/2609.01060/) (2026)

## Abstract (arXiv)

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 1024×1024×448, 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: urlhttps://github.com/hyperspectral-skin/Hyper-Skin-2023.