FaceSkin: A Privacy Preserving Facial skin patch Dataset for multi
  Attributes classification
Qiushi Guo Shisha Liao
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.