An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces

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1 Knowledge Engineering and Decision Science, Kharazmi University, Tehran, Tehran, IRAN, ISLAMIC REPUBLIC OF
2 Mathematics & Computer Science, Kharazmi University, Tehran, Tehran, IRAN, ISLAMIC REPUBLIC OF
DOI
10.7287/peerj.preprints.27488v1
Published
Accepted
Subject Areas
Computer Vision, Data Mining and Machine Learning
Keywords
Skin detection, Skin segmentation, Morphology, Support vector machine, Machine vision, Machine learning
Copyright
© 2019 Mortazavi T. et al.
Licence
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
Cite this article
Mortazavi T. M, Ebadati E. OM. 2019. An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces. PeerJ Preprints 7:e27488v1

Abstract

Human Skin Detection is one of the most applicable methods in human detection, face detection and so many other detections. These processes can be used in a wide spectrum like industry, medicine, security, etc. The objective of this work is to provide an accurate and efficient method to detect human skin in images. This method can detect and classify skin pixels and reduce the size of images. With the use of RGB and YCbCr color spaces, proposed approach can localize a Region Of Interest (ROI) that contains skin pixels. This method consists of three steps. In the first stage, pre-processing an image like normalization, detecting skin range from the dataset, etc. is done. In the second stage, the proposed method detects candidate’s pixels that are in the range of skin color. In the third stage, with the use of a classifier, it decreases unwanted pixels and areas to decrease the accuracy of the region. The results show 97% sensitivity and 85% specificity for support vector machine classifier.

Author Comment

This is a submission to PeerJ Computer Science for review.

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