Face recognition based on singular valued decomposition and back progagation neural network

Face recognition based on singular valued decomposition and back progagation neural network

This paper presents a developed face recognition system. The method used singular valued decomposition as images feature extractor and back propagation neural networks as its classifier. The back propagation training parameters are varied in order to find the best parameter with the highest...

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Main Authors: Rasied, Thaahirah S. M., Khalifa, Othman Omran, Kamarudin, Yuslina
Format: Conference or Workshop Item
Language:English
Published: 2005
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/1/04977212.pdf
id iium-5925
recordtype eprints
spelling iium-59252012-04-03T08:10:15Z http://irep.iium.edu.my/5925/ Face recognition based on singular valued decomposition and back progagation neural network Rasied, Thaahirah S. M. Khalifa, Othman Omran Kamarudin, Yuslina T Technology (General) This paper presents a developed face recognition system. The method used singular valued decomposition as images feature extractor and back propagation neural networks as its classifier. The back propagation training parameters are varied in order to find the best parameter with the highest performance accuracy. The results from experiments have showed that combinations of both methodologies give good recognition rate and therefore considered as an effective face recognition system. 2005 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/5925/1/04977212.pdf Rasied, Thaahirah S. M. and Khalifa, Othman Omran and Kamarudin, Yuslina (2005) Face recognition based on singular valued decomposition and back progagation neural network. In: 1st International Conference on Computers, Communications, & Signal Processing with Special Track on Biomedical Engineering, 2005, (CCSP 2005), 14-16 November 2005. http://dx.doi.org/10.1109/CCSP.2005.4977212 doi:10.1109/CCSP.2005.4977212
repository_type Digital Repository
institution_category Local University
institution International Islamic University Malaysia
building IIUM Repository
collection Online Access
language English
topic T Technology (General)
spellingShingle T Technology (General)
Rasied, Thaahirah S. M.
Khalifa, Othman Omran
Kamarudin, Yuslina
Face recognition based on singular valued decomposition and back progagation neural network
description This paper presents a developed face recognition system. The method used singular valued decomposition as images feature extractor and back propagation neural networks as its classifier. The back propagation training parameters are varied in order to find the best parameter with the highest performance accuracy. The results from experiments have showed that combinations of both methodologies give good recognition rate and therefore considered as an effective face recognition system.
format Conference or Workshop Item
author Rasied, Thaahirah S. M.
Khalifa, Othman Omran
Kamarudin, Yuslina
author_facet Rasied, Thaahirah S. M.
Khalifa, Othman Omran
Kamarudin, Yuslina
author_sort Rasied, Thaahirah S. M.
title Face recognition based on singular valued decomposition and back progagation neural network
title_short Face recognition based on singular valued decomposition and back progagation neural network
title_full Face recognition based on singular valued decomposition and back progagation neural network
title_fullStr Face recognition based on singular valued decomposition and back progagation neural network
title_full_unstemmed Face recognition based on singular valued decomposition and back progagation neural network
title_sort face recognition based on singular valued decomposition and back progagation neural network
publishDate 2005
url http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/
http://irep.iium.edu.my/5925/1/04977212.pdf
first_indexed 2023-09-18T20:14:44Z
last_indexed 2023-09-18T20:14:44Z
_version_ 1777407712303251456

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