Accurate Pixel-Wise Skin Segmentation Using Shallow Fully Convolutional Neural Network

被引:6
|
作者
Minhas, Komal [1 ]
Khan, Tariq M. [2 ]
Arsalan, Muhammad [3 ]
Naqvi, Syed Saud [2 ]
Ahmed, Mansoor [1 ]
Khan, Haroon Ahmed [2 ]
Haider, Muhammad Adnan [3 ]
Haseeb, Abdul [4 ]
机构
[1] COMSATS Univ Islamabad, Dept Comp Sci, Islamabad 45550, Pakistan
[2] COMSATS Univ Islamabad, Dept Elect & Comp Engn, Islamabad 45550, Pakistan
[3] Dongguk Univ, Div Elect & Elect Engn, Seoul 100715, South Korea
[4] Inst Space Technol IST, Dept Elect Engn, Islamabad 44000, Pakistan
来源
IEEE ACCESS | 2020年 / 8卷 / 08期
关键词
Skin; Image color analysis; Image segmentation; Semantics; Task analysis; Neural networks; Lighting; Skin segmentation; semantic segmentation; low-level semantic information; deepLabv3+; FACE;
D O I
10.1109/ACCESS.2020.3019183
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Skin segmentation plays an important role in human activity recognition, video surveillance, hand gesture identification, face detection, human tracking and robotic surgery. The accurate segmentation of the skin is necessary to recognize the human activity. Segmentation of skin is easy to realize in ideal situations because of similar backgrounds. But it becomes complicated because of presence of skin-like pixels, background illuminations, and certain changes in environment. These problems are addressed by incorporating preprocessing stages in current studies, but this raises the total cost of the system. However, there are some limitations associated with these methods in terms of accuracy and processing speed. In this work, we propose a skin semantic segmentation network (SSS-Net) that is able to capture the multi-scale contextual information and refines the segmentation results especially along object boundaries. Moreover our network helps to reduce the cost of the preprocessing as well. We have performed experiments on the five open datasets of human activity recognition for the segmentation of skin. Experimental results show SSS-Net outperforms the state-of-the-art methods in skin segmentation in terms of accuracies.
引用
收藏
页码:156314 / 156327
页数:14
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