Feature-based face recognition system using utilized artificial neural network

This project aims to reduce the effect of critical conditions such as excessive illumination, facial expressions, hairstyles, beard and moustache which have affected the performance of face recognition since ages ago. The main contributions of this project are the automatic algorithms for mouth de...

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Main Author:	Chai, Tong Yuen
Format:	Thesis
Language:	English
Subjects:	Face recognition Algorithms Artificial intelligent (AI) Computer vision Artificial neural network Facial features cropping
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/2/Full%20Text.pdf
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id	my-unimap-9886
record_format	uketd_dc
spelling	my-unimap-98862010-10-19T04:43:59Z Feature-based face recognition system using utilized artificial neural network Chai, Tong Yuen This project aims to reduce the effect of critical conditions such as excessive illumination, facial expressions, hairstyles, beard and moustache which have affected the performance of face recognition since ages ago. The main contributions of this project are the automatic algorithms for mouth detection, facial features cropping and face classification. First, the algorithm will detect a human face and irises. Second, the mouth region is estimated by using geometric calculation based on the irises positions. A proposed algorithm which combines RGB color map and corner detection techniques will detect the mouth corners. Then, the proposed features cropping system will crop the detected iris and mouth automatically. These features are fed into the backpropagation neural network. The proposed architecture contains two neural networks. The second network merges the results from template matching and first neural network to reduce wrong recognition rate and improve the performance of neural network. The proposed automatic feature-based face recognition system has efficiency more than 95% under the stated critical conditions. All the experiment results are studied to prove the quality and uniqueness of this research. Universiti Malaysia Perlis 2009 Thesis en http://dspace.unimap.edu.my/123456789/9886 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/1/Page%201-24.pdf 95c4ac11da1a690e6fc4f93b25662806 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/2/Full%20Text.pdf f9f4088e17f9bd55ba9da7a9929089f8 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/3/license.txt 552a55391e6fe148f07f38c1bec22221 Face recognition Algorithms Artificial intelligent (AI) Computer vision Artificial neural network Facial features cropping School of Mechatronics Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
topic	Face recognition Algorithms Artificial intelligent (AI) Computer vision Artificial neural network Facial features cropping
spellingShingle	Face recognition Algorithms Artificial intelligent (AI) Computer vision Artificial neural network Facial features cropping Chai, Tong Yuen Feature-based face recognition system using utilized artificial neural network
description	This project aims to reduce the effect of critical conditions such as excessive illumination, facial expressions, hairstyles, beard and moustache which have affected the performance of face recognition since ages ago. The main contributions of this project are the automatic algorithms for mouth detection, facial features cropping and face classification. First, the algorithm will detect a human face and irises. Second, the mouth region is estimated by using geometric calculation based on the irises positions. A proposed algorithm which combines RGB color map and corner detection techniques will detect the mouth corners. Then, the proposed features cropping system will crop the detected iris and mouth automatically. These features are fed into the backpropagation neural network. The proposed architecture contains two neural networks. The second network merges the results from template matching and first neural network to reduce wrong recognition rate and improve the performance of neural network. The proposed automatic feature-based face recognition system has efficiency more than 95% under the stated critical conditions. All the experiment results are studied to prove the quality and uniqueness of this research.
format	Thesis
author	Chai, Tong Yuen
author_facet	Chai, Tong Yuen
author_sort	Chai, Tong Yuen
title	Feature-based face recognition system using utilized artificial neural network
title_short	Feature-based face recognition system using utilized artificial neural network
title_full	Feature-based face recognition system using utilized artificial neural network
title_fullStr	Feature-based face recognition system using utilized artificial neural network
title_full_unstemmed	Feature-based face recognition system using utilized artificial neural network
title_sort	feature-based face recognition system using utilized artificial neural network
granting_institution	Universiti Malaysia Perlis
granting_department	School of Mechatronics Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/9886/2/Full%20Text.pdf
_version_	1747836783003959296

Feature-based face recognition system using utilized artificial neural network

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