Design and development of skin detection model based deep learning on different skin tones

The aim of this study is to design and develop a systematic dataset for multiple skin tones and toanalyse the reasons behind misclassifications of skin and non-skin, using different deep learningmodels, colour spaces, and different optimisation parameters. Related academic literature havecited three...

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Main Author:	Al-Qaysi, Mohammed Ahmed Chyad
Format:	thesis
Language:	eng
Published:	2021
Subjects:	QA76 Computer software
Online Access:	https://ir.upsi.edu.my/detailsg.php?det=5828
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institution	Universiti Pendidikan Sultan Idris
collection	UPSI Digital Repository
language	eng
topic	QA76 Computer software
spellingShingle	QA76 Computer software Al-Qaysi, Mohammed Ahmed Chyad Design and development of skin detection model based deep learning on different skin tones
description	The aim of this study is to design and develop a systematic dataset for multiple skin tones and toanalyse the reasons behind misclassifications of skin and non-skin, using different deep learningmodels, colour spaces, and different optimisation parameters. Related academic literature havecited three problems, namely data-related issues (e.g. skin-like), data volume (e.g. large volumerequires high computer source), and technical issues (e.g. optimising parameters). Twoarticles on Deep Learning (DL) for skin detection failed to address the issues extensively.DL foundation is a training dataset and the quality of training depends on the quality ofthe data-input. To address the issues, a systematic dataset consisting of 17 million patcheswas created for multiple skin tones with (skin-like) images. The dataset was then converted intodifferent colour spaces with multiple labels that characterise different scenarios, runningdifferent DL. Experimentally utilised YCbCr and CNN present high performance of binaryand multi-class classifications. Binary classification of skin and skin-like resulted in 98% andmulti-class classification of four classes 84% and 69% for five classes respectively. Furthermore,a binary classification between skin tone and skin-like (e.g. black skin tone and blackskin-like) resulted in 97%, 81%, 60%, and 51% for black, brown, medium, and fairconsequently. From empirical experiment, darker skin tone is a better classification accuracyfollowed by optimising parameters (Hidden-Layers, Neurons, Activations-Functions, Optimiser,Initialiser, Data-Input, and Data-Size). A hybrid CNN-RNN benchmark improves the accuracy by99% compared with 98%, and 97% for SAE compared to 91% as reported. By studying different skinscenarios, one can analyse the reasons behind overlapping between skin and skin-tones.This is a promising study for further research by developing and applying a generalised versionof skin detector with different applications.
format	thesis
qualification_name
qualification_level	Doctorate
author	Al-Qaysi, Mohammed Ahmed Chyad
author_facet	Al-Qaysi, Mohammed Ahmed Chyad
author_sort	Al-Qaysi, Mohammed Ahmed Chyad
title	Design and development of skin detection model based deep learning on different skin tones
title_short	Design and development of skin detection model based deep learning on different skin tones
title_full	Design and development of skin detection model based deep learning on different skin tones
title_fullStr	Design and development of skin detection model based deep learning on different skin tones
title_full_unstemmed	Design and development of skin detection model based deep learning on different skin tones
title_sort	design and development of skin detection model based deep learning on different skin tones
granting_institution	Universiti Pendidikan Sultan Idris
granting_department	Fakulti Seni, Komputeran dan Industri Kreatif
publishDate	2021
url	https://ir.upsi.edu.my/detailsg.php?det=5828
_version_	1747833231705636864
spelling	oai:ir.upsi.edu.my:58282021-04-13 Design and development of skin detection model based deep learning on different skin tones 2021 Al-Qaysi, Mohammed Ahmed Chyad QA76 Computer software The aim of this study is to design and develop a systematic dataset for multiple skin tones and toanalyse the reasons behind misclassifications of skin and non-skin, using different deep learningmodels, colour spaces, and different optimisation parameters. Related academic literature havecited three problems, namely data-related issues (e.g. skin-like), data volume (e.g. large volumerequires high computer source), and technical issues (e.g. optimising parameters). Twoarticles on Deep Learning (DL) for skin detection failed to address the issues extensively.DL foundation is a training dataset and the quality of training depends on the quality ofthe data-input. To address the issues, a systematic dataset consisting of 17 million patcheswas created for multiple skin tones with (skin-like) images. The dataset was then converted intodifferent colour spaces with multiple labels that characterise different scenarios, runningdifferent DL. Experimentally utilised YCbCr and CNN present high performance of binaryand multi-class classifications. Binary classification of skin and skin-like resulted in 98% andmulti-class classification of four classes 84% and 69% for five classes respectively. Furthermore,a binary classification between skin tone and skin-like (e.g. black skin tone and blackskin-like) resulted in 97%, 81%, 60%, and 51% for black, brown, medium, and fairconsequently. From empirical experiment, darker skin tone is a better classification accuracyfollowed by optimising parameters (Hidden-Layers, Neurons, Activations-Functions, Optimiser,Initialiser, Data-Input, and Data-Size). A hybrid CNN-RNN benchmark improves the accuracy by99% compared with 98%, and 97% for SAE compared to 91% as reported. By studying different skinscenarios, one can analyse the reasons behind overlapping between skin and skin-tones.This is a promising study for further research by developing and applying a generalised versionof skin detector with different applications. 2021 thesis https://ir.upsi.edu.my/detailsg.php?det=5828 https://ir.upsi.edu.my/detailsg.php?det=5828 text eng closedAccess Doctoral Universiti Pendidikan Sultan Idris Fakulti Seni, Komputeran dan Industri Kreatif Abdullah-Al-Wadud, M., & Chae, O. (2008). Skin segmentation using color distance map and water-flowproperty. Paper presented at the Information Assurance and Security, 2008. ISIAS'08. FourthInternational Conference on.Ahmadi, E., Garmsirian, F., & Azimifar, Z. (2012). A discriminative fusion framework for skindetection. Paper presented at the Artificial Intelligence and Signal Processing (AISP),2012 16th CSI International Symposium on.Aibinu, A. M., Shafie, A. A., & Salami, M. J. E. (2012). 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