  Human skin detection in images is a widely studied topic of Computer Vision
for which it is commonly accepted that analysis of pixel color or local patches
may suffice. This is because skin regions appear to be relatively uniform and
many argue that there is a small chromatic variation among different samples.
However, we found that there are strong biases in the datasets commonly used to
train or tune skin detection methods. Furthermore, the lack of contextual
information may hinder the performance of local approaches. In this paper we
present a comprehensive evaluation of holistic and local Convolutional Neural
Network (CNN) approaches on in-domain and cross-domain experiments and compare
with state-of-the-art pixel-based approaches. We also propose a combination of
inductive transfer learning and unsupervised domain adaptation methods, which
are evaluated on different domains under several amounts of labelled data
availability. We show a clear superiority of CNN over pixel-based approaches
even without labelled training samples on the target domain. Furthermore, we
provide experimental support for the counter-intuitive superiority of holistic
over local approaches for human skin detection.
