# FaceSkin: A Privacy Preserving Facial skin patch Dataset for multi Attributes classification

arXiv: https://arxiv.org/abs/2308.04765 · HTML: https://mlsanity.com/paper/2308.04765/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 19 references that are in the map.
- First posted on arXiv 9 Aug 2023.

## Builds on (most cited first, 10 of 10)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Describing Textures in the Wild](https://mlsanity.com/paper/1311.3618/) (2013)
- [MediaPipe: A Framework for Building Perception Pipelines](https://mlsanity.com/paper/1906.08172/) (2019)
- [A deep learning system for differential diagnosis of skin diseases](https://mlsanity.com/paper/1909.05382/) (2019)
- [CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations](https://mlsanity.com/paper/2007.12342/) (2020)
- [Material Recognition in the Wild with the Materials in Context Database](https://mlsanity.com/paper/1412.0623/) (2014)

## Built on by (most cited first, 1 of 1)

- [Robust Skin Color Driven Privacy Preserving Face Recognition via Function Secret Sharing](https://mlsanity.com/paper/2407.05045/) (2024)

## Abstract (arXiv)

Human facial skin images contain abundant textural information that can serve as valuable features for attribute classification, such as age, race, and gender. Additionally, facial skin images offer the advantages of easy collection and minimal privacy concerns. However, the availability of well-labeled human skin datasets with a sufficient number of images is limited. To address this issue, we introduce a dataset called FaceSkin, which encompasses a diverse range of ages and races. Furthermore, to broaden the application scenarios, we incorporate synthetic skin-patches obtained from 2D and 3D attack images, including printed paper, replays, and 3D masks. We evaluate the FaceSkin dataset across distinct categories and present experimental results demonstrating its effectiveness in attribute classification, as well as its potential for various downstream tasks, such as Face anti-spoofing and Age estimation.