  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.
