An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces
- 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
- 2019. An improved human skin detection and localization by using machine learning techniques in RGB and YCbCr color spaces. PeerJ Preprints 7:e27488v1 https://doi.org/10.7287/peerj.preprints.27488v1
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.