Efficient and Low Color Information Dependency Skin Segmentation Model

Hojoon You, Kunyoung Lee, Jaemu Oh and Eui Chul Lee ()
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Hojoon You: Department of AI & Informatics, Graduate School, Sangmyung University, Seoul 03016, Republic of Korea
Kunyoung Lee: Department of Computer Science, Graduate School, Sangmyung University, Seoul 03016, Republic of Korea
Jaemu Oh: Department of AI & Informatics, Graduate School, Sangmyung University, Seoul 03016, Republic of Korea
Eui Chul Lee: Department of Human-Centered Artificial Intelligence, Graduate School, Sangmyung University, Seoul 03016, Republic of Korea

Mathematics, 2023, vol. 11, issue 9, 1-15

Abstract: Skin segmentation involves segmenting the human skin region in an image. It is a preprocessing technique mainly used in many applications such as face detection, hand gesture recognition, and remote biosignal measurements. As the performance of skin segmentation directly affects the performance of these applications, precise skin segmentation methods have been studied. However, previous skin segmentation methods are unsuitable for real-world environments because they rely heavily on color information. In addition, deep-learning-based skin segmentation methods incur high computational costs, even though skin segmentation is mainly used for preprocessing. This study proposes a lightweight skin segmentation model with a high performance. Additionally, we used data augmentation techniques that modify the hue, saturation, and values, allowing the model to learn texture or contextual information better without relying on color information. Our proposed model requires 1.09M parameters and 5.04 giga multiply-accumulate. Through experiments, we demonstrated that our proposed model shows high performance with an F-score of 0.9492 and consistent performance even for modified images. Furthermore, our proposed model showed a fast processing speed of approximately 68 fps, based on 3 × 512 × 512 images and an NVIDIA RTX 2080TI GPU (11GB VRAM) graphics card.

Keywords: skin segmentation; preprocessing; mobile vision transformer; low color information dependency (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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