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A human face recognition using Alyuda Neurointelligence

Link to publisher's homepage at http://amci.unimap.edu.my

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Main Authors: Norpah, Mahat, Afifah Sakinah, Mohamad Zuki
Other Authors: norpah020@uitm.edu.my
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2020
Subjects:
Neural network
Comparative study
Human face recognition
Algorithms
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63715
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spelling my.unimap-637152020-01-03T13:43:01Z A human face recognition using Alyuda Neurointelligence Norpah, Mahat Afifah Sakinah, Mohamad Zuki norpah020@uitm.edu.my Neural network Comparative study Human face recognition Algorithms Link to publisher's homepage at http://amci.unimap.edu.my Nowadays, face recognition has been one of the most popular studies. It is considered as a highly interesting topic to do a study on. With the advancement of today’s technology, face recognition has been used in a wide range of areas. For instance, face recognition is very common in the security industry. The main idea of this study is to identify the best algorithm with the smallest mean squared error (MSE). The analyses were carried out to compare the algorithms with the smallest mean squared error and to improve the previous research on face recognition based on artificial neural networks. The study on face recognition data and their evaluation by neural networks is important in detecting human faces. This study was conducted by using 45 different face images. The architecture for the network was obtained by Alyuda Neurointelligence where the most popular learning algorithm such as Quick Propagation, Conjugate Gradient Descent, Quasi Newton, Limited Memory Quasi Newton, Levenberg-Marquadt, Online Back Propagation and Batch Back Propagation have been implemented and tested to measure the percentage of success. The results indicated that the Adaptive Techniques were extremely useful pattern recognition especially in identifying human faces. The Limited Memory Quasi-Newton becomes the most suitable algorithm to train the human face recognition data with the smallest MSE. Furthermore, this study has shown a strong positive relationship proven by the R-squared and correlation coefficient for all algorithms. 2020-01-03T13:43:01Z 2020-01-03T13:43:01Z 2019-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.8 (1), 2019, pages 67-76 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63715 http://amci.unimap.edu.my en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Neural network
Comparative study
Human face recognition
Algorithms
spellingShingle Neural network
Comparative study
Human face recognition
Algorithms
Norpah, Mahat
Afifah Sakinah, Mohamad Zuki
A human face recognition using Alyuda Neurointelligence
description Link to publisher's homepage at http://amci.unimap.edu.my
author2 norpah020@uitm.edu.my
author_facet norpah020@uitm.edu.my
Norpah, Mahat
Afifah Sakinah, Mohamad Zuki
format Article
author Norpah, Mahat
Afifah Sakinah, Mohamad Zuki
author_sort Norpah, Mahat
title A human face recognition using Alyuda Neurointelligence
title_short A human face recognition using Alyuda Neurointelligence
title_full A human face recognition using Alyuda Neurointelligence
title_fullStr A human face recognition using Alyuda Neurointelligence
title_full_unstemmed A human face recognition using Alyuda Neurointelligence
title_sort human face recognition using alyuda neurointelligence
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2020
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/63715
_version_ 1670805815073701888
score 13.222552

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