Classification of thumbprint using Artificial Neural Network (ANN)

This paper presents the classification of thumbprint using Artificial Neural Network (ANN). The ANN technique is conducted in order to improve the minutia extraction techniques. The classification of thumbprint is used in order to match the person's identification and train the data by using AN...

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Published in:Proceedings - 2011 IEEE International Conference on System Engineering and Technology, ICSET 2011
Main Author: Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
Format: Conference paper
Language:English
Published: 2011
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052842979&doi=10.1109%2fICSEngT.2011.5993456&partnerID=40&md5=6bba3b79056c460867a010870c2fea36
id 2-s2.0-80052842979
spelling 2-s2.0-80052842979
Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
Classification of thumbprint using Artificial Neural Network (ANN)
2011
Proceedings - 2011 IEEE International Conference on System Engineering and Technology, ICSET 2011


10.1109/ICSEngT.2011.5993456
https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052842979&doi=10.1109%2fICSEngT.2011.5993456&partnerID=40&md5=6bba3b79056c460867a010870c2fea36
This paper presents the classification of thumbprint using Artificial Neural Network (ANN). The ANN technique is conducted in order to improve the minutia extraction techniques. The classification of thumbprint is used in order to match the person's identification and train the data by using ANN. The data of thumbprint is taken from five different people. For each person, 30 thumbprint data is taken. All the data will be the input for artificial neural network for learning purposes. All the data will be adjusted using Corel. Then by using Matlab software, the data is trained and tested according to the artificial neural network program. This is to ensure that the output is matched with the input data. The result will be shown in the graph to identify whether the system is sufficient for training the thumbprint data. It can also be used for person identification. Therefore, the result shows that the system is accurate in training the data. © 2011 IEEE.


English
Conference paper

author Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
spellingShingle Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
Classification of thumbprint using Artificial Neural Network (ANN)
author_facet Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
author_sort Naim N.F.; Yassin A.I.M.; Zakaria N.B.; Wahab N.A.
title Classification of thumbprint using Artificial Neural Network (ANN)
title_short Classification of thumbprint using Artificial Neural Network (ANN)
title_full Classification of thumbprint using Artificial Neural Network (ANN)
title_fullStr Classification of thumbprint using Artificial Neural Network (ANN)
title_full_unstemmed Classification of thumbprint using Artificial Neural Network (ANN)
title_sort Classification of thumbprint using Artificial Neural Network (ANN)
publishDate 2011
container_title Proceedings - 2011 IEEE International Conference on System Engineering and Technology, ICSET 2011
container_volume
container_issue
doi_str_mv 10.1109/ICSEngT.2011.5993456
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052842979&doi=10.1109%2fICSEngT.2011.5993456&partnerID=40&md5=6bba3b79056c460867a010870c2fea36
description This paper presents the classification of thumbprint using Artificial Neural Network (ANN). The ANN technique is conducted in order to improve the minutia extraction techniques. The classification of thumbprint is used in order to match the person's identification and train the data by using ANN. The data of thumbprint is taken from five different people. For each person, 30 thumbprint data is taken. All the data will be the input for artificial neural network for learning purposes. All the data will be adjusted using Corel. Then by using Matlab software, the data is trained and tested according to the artificial neural network program. This is to ensure that the output is matched with the input data. The result will be shown in the graph to identify whether the system is sufficient for training the thumbprint data. It can also be used for person identification. Therefore, the result shows that the system is accurate in training the data. © 2011 IEEE.
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