Weakly Supervised Human Skin Segmentation using Guidance Attention Mechanisms

TL;DR

This paper introduces a weakly supervised, attention-based neural network for human skin segmentation that effectively balances accuracy, robustness, and computational efficiency, suitable for real-time applications.

Contribution

It proposes a novel attention-guided architecture with weak supervision to improve skin segmentation accuracy while reducing computational requirements.

Findings

Outperforms state-of-the-art methods on benchmark datasets.

Achieves real-time performance with high accuracy.

Effectively handles noisy labels in training data.

Abstract

Human skin segmentation is a crucial task in computer vision and biometric systems, yet it poses several challenges such as variability in skin color, pose, and illumination. This paper presents a robust data-driven skin segmentation method for a single image that addresses these challenges through the integration of contextual information and efficient network design. In addition to robustness and accuracy, the integration into real-time systems requires a careful balance between computational power, speed, and performance. The proposed method incorporates two attention modules, Body Attention and Skin Attention, that utilize contextual information to improve segmentation results. These modules draw attention to the desired areas, focusing on the body boundaries and skin pixels, respectively. Additionally, an efficient network architecture is employed in the encoder part to minimize computational power while retaining high performance. To handle the issue of noisy labels in skin datasets, the proposed method uses a weakly supervised training strategy, relying on the Skin Attention module. The results of this study demonstrate that the proposed method is comparable to, or outperforms, state-of-the-art methods on benchmark datasets.