AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification...

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Autores principales:	LAWRENCE OMOTOSHO, IBRAHIM OGUNDOYIN, OLAJIDE ADEBAYO, JOSHUA OYENIYI
Formato:	article
Lenguaje:	EN
Publicado:	Alma Mater Publishing House "Vasile Alecsandri" University of Bacau 2021
Materias:	multimodal, biometric system, convolutional neural network Technology T Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/14a8a125934846ada4a74bcb40f4ba3d
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id	oai:doaj.org-article:14a8a125934846ada4a74bcb40f4ba3d
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spelling	oai:doaj.org-article:14a8a125934846ada4a74bcb40f4ba3d2021-12-02T19:52:40ZAN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK2068-75592344-4932https://doaj.org/article/14a8a125934846ada4a74bcb40f4ba3d2021-10-01T00:00:00Zhttp://www.jesr.ub.ro/1/article/view/276https://doaj.org/toc/2068-7559https://doaj.org/toc/2344-4932 Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%. LAWRENCE OMOTOSHOIBRAHIM OGUNDOYINOLAJIDE ADEBAYOJOSHUA OYENIYIAlma Mater Publishing House "Vasile Alecsandri" University of Bacauarticlemultimodal, biometric system, convolutional neural networkTechnologyTEngineering (General). Civil engineering (General)TA1-2040ENJournal of Engineering Studies and Research, Vol 27, Iss 2 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	multimodal, biometric system, convolutional neural network Technology T Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	multimodal, biometric system, convolutional neural network Technology T Engineering (General). Civil engineering (General) TA1-2040 LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
description	Multimodal biometric system combines more than one biometric modality into a single method in order, to overcome the limitations of unimodal biometrics system. In multimodal biometrics system, the utilization of different algorithms for feature extraction, fusion at feature level and classification often to complexity and make fused biometrics features larger in dimensions. In this paper, we developed a face-iris multimodal biometric recognition system based on convolutional neural network for feature extraction, fusion at feature level, training and matching to reduce dimensionality, error rate and improve the recognition accuracy suitable for an access control. Convolutional Neural Network is based on deep supervised learning model and was employed for training, classification, and testing of the system. The images are preprocessed to a standard normalization and then flow into couples of convolutional layers. The developed multimodal biometrics system was evaluated on a dataset of 700 iris and facial images, the training database contain 600 iris and face images, 100 iris and face images were used for testing. Experimental result shows that at the learning rate of 0.0001, the multimodal system has a performance recognition accuracy (RA) of 98.33% and equal error rate (ERR) of 0.0006%.
format	article
author	LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI
author_facet	LAWRENCE OMOTOSHO IBRAHIM OGUNDOYIN OLAJIDE ADEBAYO JOSHUA OYENIYI
author_sort	LAWRENCE OMOTOSHO
title	AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
title_short	AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
title_full	AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
title_fullStr	AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
title_full_unstemmed	AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK
title_sort	enhanced multimodal biometric system based on convolutional neural network
publisher	Alma Mater Publishing House "Vasile Alecsandri" University of Bacau
publishDate	2021
url	https://doaj.org/article/14a8a125934846ada4a74bcb40f4ba3d
work_keys_str_mv	AT lawrenceomotosho anenhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT ibrahimogundoyin anenhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT olajideadebayo anenhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT joshuaoyeniyi anenhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT lawrenceomotosho enhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT ibrahimogundoyin enhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT olajideadebayo enhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork AT joshuaoyeniyi enhancedmultimodalbiometricsystembasedonconvolutionalneuralnetwork
_version_	1718375884417662976

AN ENHANCED MULTIMODAL BIOMETRIC SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

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