Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features

Face recognition is one of the technologies used for assets protection. Face recognition also presents a challenging problem in the field of image and computer vision and has been used for the application such as face tracking and personal identification. It also frequently used in a security syste...

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Formato:	article
Lenguaje:	EN
Publicado:	Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis 2018
Materias:	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995
Acceso en línea:	https://doaj.org/article/ba77672bd4b6488d95e044eb3d72ee4b
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spelling	oai:doaj.org-article:ba77672bd4b6488d95e044eb3d72ee4b2021-11-06T02:25:59ZHellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features2600-8793https://doaj.org/article/ba77672bd4b6488d95e044eb3d72ee4b2018-11-01T00:00:00Zhttp://repeater.my/index.php/jcrinn/article/view/75https://doaj.org/toc/2600-8793 Face recognition is one of the technologies used for assets protection. Face recognition also presents a challenging problem in the field of image and computer vision and has been used for the application such as face tracking and personal identification. It also frequently used in a security system such as a security camera in airport, banks, and offices. Practically, there are problems in improving face recognition performance, particularly for gender identification. It is very difficult to differentiate the person based on face appearance from different poses, lighting, expressions, aging and illumination. Sometimes it is also difficult to identify the shape of human faces because different people have a different structure of faces. This study used image retrieved from Student Information Management Systems (SIMS)from 10 male and 43 female students who're taking MAT530. The image was then generated 12 geometric landmarks using TI nspire software. The main goal of this research is to classify the gender through the images of faces and to resolve for imbalance data using Hellinger Distance Decision Tree (HDDT) classifier. This classifier was proposed as an alternative to decision tree technique which used Hellinger Distance as the splitting criteria. The result from the validation split shows that percentage split at 40% produced the highest value of accuracy rate at 77.2727% and has the most significant value of sensitivity and specificity. Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA PerlisarticleProbabilities. Mathematical statisticsQA273-280TechnologyTTechnology (General)T1-995ENJournal of Computing Research and Innovation, Vol 3, Iss 4 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995
spellingShingle	Probabilities. Mathematical statistics QA273-280 Technology T Technology (General) T1-995 Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
description	Face recognition is one of the technologies used for assets protection. Face recognition also presents a challenging problem in the field of image and computer vision and has been used for the application such as face tracking and personal identification. It also frequently used in a security system such as a security camera in airport, banks, and offices. Practically, there are problems in improving face recognition performance, particularly for gender identification. It is very difficult to differentiate the person based on face appearance from different poses, lighting, expressions, aging and illumination. Sometimes it is also difficult to identify the shape of human faces because different people have a different structure of faces. This study used image retrieved from Student Information Management Systems (SIMS)from 10 male and 43 female students who're taking MAT530. The image was then generated 12 geometric landmarks using TI nspire software. The main goal of this research is to classify the gender through the images of faces and to resolve for imbalance data using Hellinger Distance Decision Tree (HDDT) classifier. This classifier was proposed as an alternative to decision tree technique which used Hellinger Distance as the splitting criteria. The result from the validation split shows that percentage split at 40% produced the highest value of accuracy rate at 77.2727% and has the most significant value of sensitivity and specificity.
format	article
title	Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
title_short	Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
title_full	Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
title_fullStr	Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
title_full_unstemmed	Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features
title_sort	hellinger distance decision tree (hddt) classification of gender with imbalance statistical face features
publisher	Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Perlis
publishDate	2018
url	https://doaj.org/article/ba77672bd4b6488d95e044eb3d72ee4b
_version_	1718444004348002304

Hellinger Distance Decision Tree (HDDT) Classification of Gender with Imbalance Statistical Face Features

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