OR-Skip-Net: Outer residual skip network for skin segmentation in non-ideal situations. (1st March 2020)

• Record Type: Journal Article
• Authors: Arsalan, Muhammad; Kim, Dong Seop; Owais, Muhammad; Park, Kang Ryoung
• Is Part Of: Expert systems with applications. Volume 141(2020)
• Publication Date: 2020-03-01
• Subjects: Skin detection -- Non-ideal conditions -- Hand segmentation -- Black skin -- OR-Skip-Net; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.112922
• Languages: English
• ISSNs: 0957-4174

Abstract

Hightlights: OR-Skip-Net is an end-to-end semantic segmentation network for skin & gland segmentation. OR-Skip-Net converges faster using only 9718, 786 parameters and keeping edge information. OR-Skip-Net uniquely uses the outer residual skip paths from the encoder to directly decoder. OR-Skip-Net is tested for skin segmentation of black skin people (BSP). Trained OR-Skip-Net, algorithms, and BSP label information are made publicly available. Abstract: Skin segmentation is one of the most important tasks for human activity recognition, video monitoring, face detection, hand gesture recognition, content-based detection, adult content filtering, human tracking, and robotic surgeries. Skin segmentation in ideal situations is easy to accomplish because it is with similar backgrounds. However, the skin segmentation in non-ideal situations is complicated due to difficult background illuminations, the presence of skin-like pixels, and environmental changes. The current studies are handling the mentioned challenges by adding the preprocessing stages in their methods, which increases the overall cost of the system. In addition, prevailing segmentation studies have ignored black skin and mainly focused on white skin for their experiments. To deal with skin segmentation in challenging environments irrespective of skin color, and to eliminate the cost of the preprocessing, this paper proposes an outer residual skip connection-based deep convolutional neural network (OR-Skip-Net) which innovatively empowers the features by transferring the direct edge information from the initial layer to the end of the network. Experiments were performed on the following eight open datasets for skin segmentation in different environments: hand gesture recognition dataset, event detection dataset, laboratoire d'informatique en image et systèmes d'information dataset, in-house dataset, UT-interaction dataset, augmented multi-party interaction dataset, Pratheepan dataset, and black skin people dataset. In addition, two other experiments were performed for gland segmentation from colon cancer histology images for the diagnosis of colorectal cancer using the Warwick-QU dataset and for iris segmentation using the Noisy Iris Challenge Evaluation - Part II dataset to explore the possibility of applying our method to different applications. Experimental results showed that the proposed OR-Skip-Net outperformed existing methods in terms of skin, gland, and iris segmentation accuracies.