Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method

Human skin detection is an important preprocessing step in many applications involving images such as face detection, gesture tracking, and nudity detection. Color is a significant source of information for human skin detection, and some studies have discussed the effect of color space on skin detec...

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Main Author:	Al-Mohair, Hani Kaid Saif
Format:	Thesis
Language:	English
Published:	2017
Subjects:	T Technology
Online Access:	http://eprints.usm.my/46446/1/Development%20Of%20Human%20Skin%20Detection%20Algorithm%20Using%20Multilayer%20Perceptron%20Neural%20Network%20And%20Clustering%20Method.pdf
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spelling	my-usm-ep.464462021-11-17T03:42:14Z Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method 2017-11-01 Al-Mohair, Hani Kaid Saif T Technology TK1-9971 Electrical engineering. Electronics. Nuclear engineering Human skin detection is an important preprocessing step in many applications involving images such as face detection, gesture tracking, and nudity detection. Color is a significant source of information for human skin detection, and some studies have discussed the effect of color space on skin detection. However, there is no consensus on which color space is the most appropriate for skin color detection. In addition, good performance of such applications depends on reliable skin classifiers that must be able to discriminate between skin and non-skin pixels for a wide range of people, regardless of age, gender, or race. Many classifiers including intelligent classifiers have been utilized for human skin detection with a few limitations such as low accuracy. In this work, a comprehensive comparative study using the Multilayer Perceptron Artificial Neural Network (MLP ANN) is performed on various color spaces (RGB, normalized RGB, YCbCr, YIQ, HSV, YUV, YDbDr, and CIE Lab) to determine the optimum color space. Additionally, the effect of combining texture information with color information is investigated with the aim of boosting the performance of skin classifiers. The Differential Evolution Algorithm (DE) is used in this work to select the optimum color and texture information to achieve the optimum response. The experimental results show that the YIQ color space yields the highest separability between skin and non-skin pixels among the different color spaces tested using color features. In addition, the results reveal that combining color and texture features leads to more accurate and efficient skin detection. Based on these feature extraction results, a system based on a combination of an MLP ANN and k-means clustering which employs the YIQ color space and the statistical features of human skin as inputs is developed for human skin detection. The performance of the developed system has been compared with the existing intelligent skin detection systems. The experimental results reveal that the developed algorithm is able to achieve an accuracy of 87.82% F1-measure based on images from the ECU database. This result demonstrates that optimum feature selection and combination intelligent system are able to enhance the accuracy and reliability of human skin detection significantly. 2017-11 Thesis http://eprints.usm.my/46446/ http://eprints.usm.my/46446/1/Development%20Of%20Human%20Skin%20Detection%20Algorithm%20Using%20Multilayer%20Perceptron%20Neural%20Network%20And%20Clustering%20Method.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat Pengajian Kejuruteraan Elektrik & Elektronik
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	T Technology T Technology
spellingShingle	T Technology T Technology Al-Mohair, Hani Kaid Saif Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
description	Human skin detection is an important preprocessing step in many applications involving images such as face detection, gesture tracking, and nudity detection. Color is a significant source of information for human skin detection, and some studies have discussed the effect of color space on skin detection. However, there is no consensus on which color space is the most appropriate for skin color detection. In addition, good performance of such applications depends on reliable skin classifiers that must be able to discriminate between skin and non-skin pixels for a wide range of people, regardless of age, gender, or race. Many classifiers including intelligent classifiers have been utilized for human skin detection with a few limitations such as low accuracy. In this work, a comprehensive comparative study using the Multilayer Perceptron Artificial Neural Network (MLP ANN) is performed on various color spaces (RGB, normalized RGB, YCbCr, YIQ, HSV, YUV, YDbDr, and CIE Lab) to determine the optimum color space. Additionally, the effect of combining texture information with color information is investigated with the aim of boosting the performance of skin classifiers. The Differential Evolution Algorithm (DE) is used in this work to select the optimum color and texture information to achieve the optimum response. The experimental results show that the YIQ color space yields the highest separability between skin and non-skin pixels among the different color spaces tested using color features. In addition, the results reveal that combining color and texture features leads to more accurate and efficient skin detection. Based on these feature extraction results, a system based on a combination of an MLP ANN and k-means clustering which employs the YIQ color space and the statistical features of human skin as inputs is developed for human skin detection. The performance of the developed system has been compared with the existing intelligent skin detection systems. The experimental results reveal that the developed algorithm is able to achieve an accuracy of 87.82% F1-measure based on images from the ECU database. This result demonstrates that optimum feature selection and combination intelligent system are able to enhance the accuracy and reliability of human skin detection significantly.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Al-Mohair, Hani Kaid Saif
author_facet	Al-Mohair, Hani Kaid Saif
author_sort	Al-Mohair, Hani Kaid Saif
title	Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
title_short	Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
title_full	Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
title_fullStr	Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
title_full_unstemmed	Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method
title_sort	development of human skin detection algorithm using multilayer perceptron neural network and clustering method
granting_institution	Universiti Sains Malaysia
granting_department	Pusat Pengajian Kejuruteraan Elektrik & Elektronik
publishDate	2017
url	http://eprints.usm.my/46446/1/Development%20Of%20Human%20Skin%20Detection%20Algorithm%20Using%20Multilayer%20Perceptron%20Neural%20Network%20And%20Clustering%20Method.pdf
_version_	1747821672476442624

Development Of Human Skin Detection Algorithm Using Multilayer Perceptron Neural Network And Clustering Method

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