Semi-supervised Skin Detection by Network with Mutual Guidance

TL;DR

This paper introduces a semi-supervised dual-task neural network that jointly detects skin and body in images, leveraging mutual guidance to improve skin detection accuracy without extensive labeled data.

Contribution

It proposes a novel dual-task network with mutual guidance for skin detection, integrating body information as weak semantic guidance in a semi-supervised framework.

Findings

Outperforms state-of-the-art skin detection methods.

Effective use of mutual guidance improves detection accuracy.

Demonstrates robustness with limited labeled data.

Abstract

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.