Semi-supervised Skin Detection by Network with Mutual Guidance

Yi He1*   Jiayuan Shi2*   Chuan Wang2†   Haibin Huang2   Jiaming Liu2   Guanbin Li3   Risheng Liu1   Jue Wang2 

1Dalian University of Technology                  2Megvii Technology                  3Sun Yat-sen University

*Equal Contributors                  Corresponding Author

Accepted by ICCV 2019


skindet Figure: Structure of our dual-task network with mutual guidance. (a) The original network structure with mutual guidance loop. (b) The decoupled network into two stages for ease of analyzing.


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 dualtask 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.




  title={Semi-supervised Skin Detection by Network with Mutual Guidance},
  author={He, Yi and Shi, Jiayuan and Wang, Chuan and Huang, Haibin and Liu, Jiaming and Li, Guanbin and Liu, Risheng and Wang, Jue},
  journal={arXiv preprint arXiv:1908.01977},