Pre-trained based CNN model to identify finger vein

In current biometric security systems using images for security authentication, finger vein-based systems are getting special attention in particular attributable to the facts such as insurance of data confidentiality and higher accuracy. Previous studies were mostly based on finger-print, palm vein...

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Main Authors:	Fairuz, Subha, Habaebi, Mohamed Hadi, Elsheikh, Elsheikh Mohamed Ahmed
Format:	Article
Language:	English English
Published:	Universitas Ahmad Dahlan in collaboration with IAES Indonesia 2019
Subjects:	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Online Access:	http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/1/1505-3183-1-PB.pdf http://irep.iium.edu.my/73493/7/73493_Pre-trained%20based%20CNN%20model_scopus.pdf

id	iium-73493
recordtype	eprints
spelling	iium-734932019-09-11T13:02:12Z http://irep.iium.edu.my/73493/ Pre-trained based CNN model to identify finger vein Fairuz, Subha Habaebi, Mohamed Hadi Elsheikh, Elsheikh Mohamed Ahmed TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices In current biometric security systems using images for security authentication, finger vein-based systems are getting special attention in particular attributable to the facts such as insurance of data confidentiality and higher accuracy. Previous studies were mostly based on finger-print, palm vein etc. however, due to being more secure than fingerprint system and due to the fact that each person's finger vein is different from others finger vein are impossible to use to do forgery as veins reside under the skin. The system that we worked on functions by recognizing vein patterns from images of fingers which are captured using near Infrared (NIR) technology. Due to the lack of an available database, we created and used our own dataset which was pre-trained using transfer learning of AlexNet model and verification is done by applying correct as well as incorrect test images. The result of deep convolutional neural network (CNN) based several experimental results are shown with training accuracy, training loss, Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC). Universitas Ahmad Dahlan in collaboration with IAES Indonesia 2019-09 Article PeerReviewed application/pdf en http://irep.iium.edu.my/73493/1/1505-3183-1-PB.pdf application/pdf en http://irep.iium.edu.my/73493/7/73493_Pre-trained%20based%20CNN%20model_scopus.pdf Fairuz, Subha and Habaebi, Mohamed Hadi and Elsheikh, Elsheikh Mohamed Ahmed (2019) Pre-trained based CNN model to identify finger vein. Bulletin of Electrical Engineering and Informatics (BEEI), 8 (3). pp. 855-862. ISSN 2302-9285 http://www.beei.org/index.php/EEI/article/view/1505/1146 10.11591/eei.v8i3.1505
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
spellingShingle	TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices Fairuz, Subha Habaebi, Mohamed Hadi Elsheikh, Elsheikh Mohamed Ahmed Pre-trained based CNN model to identify finger vein
description	In current biometric security systems using images for security authentication, finger vein-based systems are getting special attention in particular attributable to the facts such as insurance of data confidentiality and higher accuracy. Previous studies were mostly based on finger-print, palm vein etc. however, due to being more secure than fingerprint system and due to the fact that each person's finger vein is different from others finger vein are impossible to use to do forgery as veins reside under the skin. The system that we worked on functions by recognizing vein patterns from images of fingers which are captured using near Infrared (NIR) technology. Due to the lack of an available database, we created and used our own dataset which was pre-trained using transfer learning of AlexNet model and verification is done by applying correct as well as incorrect test images. The result of deep convolutional neural network (CNN) based several experimental results are shown with training accuracy, training loss, Receiver Operating Characteristic (ROC) Curve and Area Under the Curve (AUC).
format	Article
author	Fairuz, Subha Habaebi, Mohamed Hadi Elsheikh, Elsheikh Mohamed Ahmed
author_facet	Fairuz, Subha Habaebi, Mohamed Hadi Elsheikh, Elsheikh Mohamed Ahmed
author_sort	Fairuz, Subha
title	Pre-trained based CNN model to identify finger vein
title_short	Pre-trained based CNN model to identify finger vein
title_full	Pre-trained based CNN model to identify finger vein
title_fullStr	Pre-trained based CNN model to identify finger vein
title_full_unstemmed	Pre-trained based CNN model to identify finger vein
title_sort	pre-trained based cnn model to identify finger vein
publisher	Universitas Ahmad Dahlan in collaboration with IAES Indonesia
publishDate	2019
url	http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/ http://irep.iium.edu.my/73493/1/1505-3183-1-PB.pdf http://irep.iium.edu.my/73493/7/73493_Pre-trained%20based%20CNN%20model_scopus.pdf
first_indexed	2023-09-18T21:44:12Z
last_indexed	2023-09-18T21:44:12Z
_version_	1777413340690120704

Pre-trained based CNN model to identify finger vein

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