In this paper we present a new data-driven method for robust skin detection
from a single human portrait image. Unlike previous methods, we incorporate
human body as a weak semantic guidance into this task, considering acquiring
large-scale of human labeled skin data is commonly expensive and
time-consuming. To be specific, we propose a dual-task neural network for joint
detection of skin and body via a semi-supervised learning strategy. The
dual-task network contains a shared encoder but two decoders for skin and body
separately. For each decoder, its output also serves as a guidance for its
counterpart, making both decoders mutually guided. Extensive experiments were
conducted to demonstrate the effectiveness of our network with mutual guidance,
and experimental results show our network outperforms the state-of-the-art in
skin detection.

本文提出了一种新的数据驱动方法，通过将人体作为弱语义引导，将人类皮肤检测与人体识别结合起来，构建双任务神经网络实现半监督学习，对皮肤和人体分别进行检测，并在实验中证明了该网络的有效性及优于现有最佳水平的皮肤检测能力。