  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.
