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Reseach Article

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

by Mohammed Najm Abdullah, Reem A. Hussein, Hassan A. Jeiad
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
  1. A. A. Ross, K. Nandakumar, and A. K. Jain, "Handbook of multibiometrics", vol. 6: Springer Science & Business Media, 2006.
  2. S. Garima, T. Ashwinder, "Enhance Technique in Multimodal Biometrics" International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 6, June 2014.
  3. V. Ramya , V. Kavitha , P. Sivagamasundhari, " Human face recognition using Elman neural network", International Journal of Engineering Trends and Technology (IJETT) – Volume17 Number6–Nov2014
  4. A. Cenys1, D. Gibavicius1, N. Goranin1, L. Marozas, "Genetic Algorithm based Palm Recognition Method for Biometric Authentication Systems", ELEKTRONIKA IR ELEKTROTECHNIKA, VOL. 19, NO. 2, 2013
  5. R. Raid, "Human Authentication with Earprint for Secure Telephone System", IJCCCE, Vol. 12, No. 2, 2012
  6. W. Almayyan, 'A Comparative Evalution of Feature Level Based Fusion Schemes for Multimodal Biometric Authentication', in 11th International Conference on Hybrid Intelligent Systems (HIS), Malaysia, 2011, p. 22.
  7. L. Alzoubiady, 'Multibiometric Personal Identification based on Hybrid Artificial Intelligence Technique using Serial Mode Architecture', International Journal of Computer Applications, vol. 87, no. 18, 2014.
  8. S. Gauri, K. Priyanka, "A Hybrid Approach for Fingerprint Image Enhancement", International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 6, June 2014
  9. V. Shinde and V. Mane," Pattern Recognition using Multilevel Wavelet Transform" International Journal of Computer Applications, vol. 49, no. 2, 2012.
  10. G. Madhavilatha , A. Mallaiah, T. Venkata Lakshmi, "Advanced Speaker Verification System Using Wavelets", International Journal of Engineering Research and Applications (IJERA),Vol. 1, Issue 3, pp. 891-898
  11. P. M. B. Torres, "Ultrasound Based Navigation and Control for Orthopaedic Robot Surgery," INSTITUTO SUPERIOR TÉCNICO, 2015.
  12. Ola M. Aly et al. ," An Adaptive Multimodal Biometrics System using PSO", (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 7, 2013
Index Terms

Computer Science
Information Sciences

Keywords

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