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Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network

by Mohammed Najm Abdullah, Reem A. Hussein, Hassan A. Jeiad
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 119 - Number 9
Year of Publication: 2015
Authors: Mohammed Najm Abdullah, Reem A. Hussein, Hassan A. Jeiad
10.5120/21093-3790

Mohammed Najm Abdullah, Reem A. Hussein, Hassan A. Jeiad . Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network. International Journal of Computer Applications. 119, 9 ( June 2015), 4-8. DOI=10.5120/21093-3790

@article{ 10.5120/21093-3790,
author = { Mohammed Najm Abdullah, Reem A. Hussein, Hassan A. Jeiad },
title = { Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { June 2015 },
volume = { 119 },
number = { 9 },
month = { June },
year = { 2015 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume119/number9/21093-3790/ },
doi = { 10.5120/21093-3790 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:03:35.063913+05:30
%A Mohammed Najm Abdullah
%A Reem A. Hussein
%A Hassan A. Jeiad
%T Multi-biometric System for Security Institutions using Wavelet Decomposition and Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 119
%N 9
%P 4-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Biometric systems are currently considered one of the leading methods for security and access control systems. The use of multi-biometric in verification and identification provides more reliability and accuracy for such systems. In this paper three biometric traits have been used face, iris and fingerprint for identification purpose. After preprocessing feature extraction for each trait, wavelet decomposition was used. Back-propagation neural network was employed for the training of the system. The results showed a highly accurate recognition rate after 298 epoch of training. A measurement of MSE and PSNR. With false acceptance rate (FAR) of 0% and false rejection rate (FRR) of 3% were calculated for system performance evaluation.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Multi-biometric Feature Extraction Feature Level Fusion Wavelet Decomposition Artificial Neural Network

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