Pre-trained based CNN model to identify finger vein

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

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Abstract

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).

Item Type:	Article (Journal)
Additional Information:	6727/73493
Uncontrolled Keywords:	Alexnet; Biometric identification; Convolutional neural network (CNN); Finger-vein recognition; Transfer learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User:	Dr. Mohamed Hadi Habaebi
Date Deposited:	01 Aug 2019 09:29
Last Modified:	11 Sep 2019 21:02
URI:	http://irep.iium.edu.my/id/eprint/73493

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