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Reseach Article

An Improved Intelligent Multi Biometric Authentication System

by Benson-Emenike Mercy E., Nwachukwu E.O.
Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 3 - Number 4
Year of Publication: 2015
Authors: Benson-Emenike Mercy E., Nwachukwu E.O.

Benson-Emenike Mercy E., Nwachukwu E.O. . An Improved Intelligent Multi Biometric Authentication System. Communications on Applied Electronics. 3, 4 ( November 2015), 27-38. DOI=10.5120/cae2015651930

@article{ 10.5120/cae2015651930,
author = { Benson-Emenike Mercy E., Nwachukwu E.O. },
title = { An Improved Intelligent Multi Biometric Authentication System },
journal = { Communications on Applied Electronics },
issue_date = { November 2015 },
volume = { 3 },
number = { 4 },
month = { November },
year = { 2015 },
issn = { 2394-4714 },
pages = { 27-38 },
numpages = {9},
url = { },
doi = { 10.5120/cae2015651930 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2023-09-04T19:43:27.015890+05:30
%A Benson-Emenike Mercy E.
%A Nwachukwu E.O.
%T An Improved Intelligent Multi Biometric Authentication System
%J Communications on Applied Electronics
%@ 2394-4714
%V 3
%N 4
%P 27-38
%D 2015
%I Foundation of Computer Science (FCS), NY, USA

The beginning of the 21st century was marked with events that focused on the world’s attention to public security. Increase in technological advancement gave people possibilities of information transfer and ease of physical mobility unseen before. With those possibilities comes risk of fraud, theft of personal data, or even theft of identity. One of the ways to prevent this is through biometric authentication system. Unibiometric systems rely on the evidence of a single source of information whereas multibiometric systems consolidate multiple sources of biometric evidences. Multibiometric systems, if designed properly, are able to enhance the matching performance. In this paper, Intelligent Multi-Biometric Authentication System, face and fingerprint biometric traits are used. When the images are captured, preprocessing in face and fingerprint images is done using Enhanced Extracted Face (EEF) method and Plainarized Region Of Interest (PROI) method respectively. These are fed into a Cascaded Link Feed Forward Neural Network(CLFFNN) which is a classifier trained with back-propagation algorithm. CLFFNN comprises of CLFFNN(1) used for training and CLFFNN(2) used as the main classifier. They are arranged in cascades. Afterwards, both outputs from face and fingerprints are combined using AND operation.

  1. Avinash K., Raina J. P. S. (2010) “Face Detection using Neural Network & Gabor Wavelet Transform”, International Journal of Computer Science and Technology (IJCST), Vol. 1, Issue.1, pp58-63, September 2010, ISSN : 0976 - 8491.
  2. Avinash Pokhriyal and Sushma Lehri, (2010) “A new method of fingerprint authentication using 2D wavelets,” Journal of Theoretical and Applied Information Technology
  3. Avinash Pokhriyal et. al., (2010) “MERIT: Minutiae Extraction using Rotation Invariant Thinning”, International Journal of Engineering Science and Technology Vol. 2(7), 2010, 3225-3235
  4. Belghini N., Zarghili A., Kharroubi J. and Majda A.,(2011) Sparse Random Projection and Dimensionality Reduction Applied on Face Recognition, in The Proceedings of International Conference on Intelligent Systems & Data Processing, January 2011,pp.78-82.
  5. Benson-Emenike M.E and Nwachukwu E.O.(2015) AN Efficient Image Preprocessing In An Improved Intelligent Multi Biometric Authentication System. International Journal of Applied Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of Computer Science FCS, New York, USA Volume 9 – No.6, September 2015 –
  6. Beszedes M. & Oravec M. (2014) “A System For Localization Of Human Faces In Images Using Neural Networks”, Journal Of Electrical Engineering, Vol. 56, No 7-8, pp195–199. The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.1 Fac ial expression recognition and synthesis based on an appearance model”, Signal Processing: Image Communication, Vol. 19, Issue 8, pp723-740.
  7. Bouzalmat A., Zarghili A., Kharroubi J.,(2011) “Facial Face Recognition Method Using Fourier Transform Filters Gabor and R_LDA”, IJCA Special Issue on Intelligent Systems and Data Processing, pp.18-24, 2011.
  8. Chatterjee A., Mandal S., Atiqur Rahaman G. M., and Arif A. M.,(2010) “Fingerprint Identification and Verification System by Minutiae Extraction Using Artificial Neural Network”, ISSN 2078-5828 (PRINT), ISSN 2218-5224 (Online), Volume 01, Issue 01, Manuscript Code: 100703,.
  9. Devika Chhachhiya, Amita Sharma and Manish Gupta (2013) Recapitulation on Transformations in Neural Network Back Propagation Algorithm International Journal of Information and Computation Technology ISSN 0974-2239 Volume 3, Number 4, pp. 323-328 © International Research Publications Househttp://www. /ijict.htm
  10. Erik Hjelm and Boon Kee Low (2001) Face Detection: A Survey Computer Vision and Image Understanding 83, 236–274 (2001) doi:10.1006/cviu.2001.0921, available online at
  11. Farhat Anwar, et al. (2008), "Multibiometric Systems Based Verification Technique" Faculty of Engineering, Department of ECE International Islamic, University Malaysia.
  12. Hazem M. El-Bakry (2002), Face Detection Using Neural Networks and Image Decomposition Lecture Notes in Computer Science Vol. 22, pp:205-215.
  13. Henry Rowley, Baluja S. & Kanade T. (1999) “Neural Network-Based Face Detection, Computer Vision and Pattern Recognition”, Neural Network-Based Face Detection, Pitts-burgh, Carnegie Mellon University, PhD thesis
  14. Henry A. Rowley, Shumeet Baluja &Takeo Kanade (1997) Rotation Invariant Neural Network-Based Face Detection, December, CMU-CS-97-201
  15. Iwasokun G. B. et al(2012) Fingerprint Image Enhancement: Segmentation to Thinning. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 3, No. 1
  16. Krishna Prasad P. E. S. N., Pavan Kumar K, Ramakrishna M. V. and Prasad B. D. C. N. (2013) Fusion Based Multimodal Authentication In Biometrics Using Context-Sensitive Exponent Associative Memory Model : A Novel Approach Computer Science & Information Technology (CS & IT)Jan Zizka (Eds) : CCSIT, SIPP, AISC, PDCTA – 2013 pp. 81–90, 2013. © CS & IT-CSCP
  17. Le Cun, V. et al., (2012). “Handwritten digit recognition with a back propagation network”, Neural Information Processing Systems, Vol. 2, pp. 396-404.
  18. Lourde M.R. and Khosla D. (2010) Fingerprint Identification in Biometric Security Systems International Journal of Computer and Electrical Engineering, Vol. 2, No. 5, October, 2010 1793-8163
  19. Mansaf M Elmansori & Khairuddin Omar (2011) “An Enhanced Face Detection Method Using Skin Color and Back-Propagation Neural Network”, European Journal of Scientific Research, ISSN 1450- 216X, Vol.55 No.1, pp80-86,
  20. Marasco E., (2010) “Secure Multibiometric Systems” PhD thesis submitted to University of Naples Federico
  21. Mohammad Abadi, et al, (2011) “Face Detection with the Help of Gabor Wavelets Characteristics and Neural Network Classifier”, American Journal of Scientific Research, Issue.36, pp67-76, ISSN 1450-223X,
  22. Mohammad Alia, Abdelfatah Tamimi and Omaima Al-Allaf,( 2013) "Integrated System For Monitoring And Recognizing Students During Class Session", AIRCC’s: International Journal Of Multimedia & Its Applications (IJMA), Vol.5, No.6, December, pp:45-52.
  23. Mohammadi S., Frajzadeh A. (2009) A Matching Algorithm of Minutiae for Real Time Fingerprint Identification System, World Academy of Science, Engineering and Technology 60 2009.
  24. Mohammed S Khalil et al. ( 2010) “Fingerprint Verification Based on Statistical Analysis”, IEEE,.
  25. Muhammad Imran Razzak, Rubiyah Yusof and Marzuki Khalid (2010): “Multimodal face and finger veins biometric authentication of Scientific Research and Essays “, Vol. 5(17), pp. 2529-2534, 4 September, 2010.
  26. Nayak P.K. and Narayan D. (2013) Multimodal Biometric Face and Fingerprint Recognition Using Adaptive principal Component Analysis and Multilayer Perception. International Journal of Research in Computer and Communication Technology, Vol 2, Issue 6, June-2013.
  27. Rashmi S. & Payal J., (2012), “Multi Biometric System: Secure Security System “IJREAS Volume 2, Issue 2 ISSN: 2249-3905 International Journal of Research in Engineering & Applied Sciences 182
  28. National Biometric Security Project, (2008), "Biometric Technology Application Manual" Volume One: Biometric Basics, Updated Summer.
  29. Phil Brimblecombe (2002) “Face Detection using Neural Networks”, H615 – Meng Electronic Engineering, School of Electronics and Physical Sciences, URN: 1046063
  30. Prasad K.N. (2013) “Face Detection using Neural Network”, International Journal of Computer Applications (0975 – 8887),Vol.1, No.14, pp36-39.
  31. Raj A., Bincy G., Mathu T.,( 2012)” Survey on Common Data Mining Classification techniques”, International Journal of Wisdom Based Computing, Vol. 2, Issue 1, April.
  32. Ravi Kumar R.L., Kumar S.S., Prasad J. R., Rao S.B.V., Prakash P.R.(2012) “ Fingerprint Minutia Match Using Bifurcation Technique”, S Sai Kumar et al , International Journal of Computer Science & Communication Networks,Vol 2(4), 478-486, Sep. 2012.
  33. Reetu Awasthi and Ingolikar R.A.(2013) A Study Of Biometrics Security System International Journal Of Innovative Research & Development April, 2013 Vol 2 Issue 4
  34. Ritu, Matish Garg(2014) A Review on Fingerprint-Based Identification System International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 3, March 2014 Copyright to IJARCCE 5849
  35. Sahoolizadeh, Sarikhanimoghadam and Dehghani (2008) “Face Detection using Gabor Wavelets and Neural Networks”, World Academy of Science, Engineering and Technology, Vol. 45, pp552- 554.
  36. Salim Lahmiri, (2011) “A Comparative Study Of Backpropagation Algorithms In Financial Prediction”,International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.1, No.4
  37. Sangita K Chaudahri, (2012) “An algorithm for fingerprint enhancement & matching”, National Conference on Emerging Trends in Engineering & Technology (VNCET-30 Mar’12)
  38. Sasan Golabi, Saiid Saadat, Mohammad Sadegh Helfroush, and Ashkan Tashk, (2012)“A Novel Thinning Algorithm with Fingerprint Minutiae Extraction Capability”, International Journal of Computer Theory and Engineering, Vol. 4, No. 4, August 2012
  39. Sawarkar S.D., Shubhangi Vaikole, , Shila Hivrale,Taruna Sharma,(2009) “Minutiae Extraction from Fingerprint Images”, IEEE International Advance Computing Conference, pp. 691-696, 2009.
  40. Shubhangi D C, Manohar Bali (2012): “Multi-Biometric Approaches to Face and Fingerprint Biometrics” International Journal of Engineering Research & Technology Vol. 1 Issue 5 ISSN: 2278-0181, July - 2012.30274, 2006.
  41. Simao-Zortia D. et al.,( 2003) “Image Quality and position variability assessment in minutiae-based fingerprint verification”, IEEE proceedings , , vol. 150 pp 402-408.
  42. Singh H. & Gayathri R., (2012), “Image Authentication Technique Using Fsim Algorithm” International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 Vol. 2, Issue 2, pp.1129-1133.
  43. Stefan W., Christian I. & Uwe H., (2004) “Evolutionary Optimization of Neural Networks for Face Detection”, Proceedings of the 12th European Symposium on Artificial Neural Networks, Evere, Belgium: d-side publications.
  44. Tran Binh Long, Le Hoang Thai (2012) Hybrid Multi-Biometric Person Authentication System Proceedings of the World Congress on Engineering and Computer Science 2012 Vol I WCECS 2012, October 24-26, 2012, San Francisco, USA.
  45. Gualberto Aguilar, Gabriel Sanchez (2007) “Fingerprint Recognition”IEEE,.
  46. Hudson, Hagan and Demuth, (2012) Neural Network Toolbox™ User’s Guide R2012a, The MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760-2098, ,
  47. Omaima N. A. AL-Allaf (2014) Review Of Face Detection Systems Based Artificial Neural Networks Algorithms The International Journal of Multimedia & Its Applications (IJMA) Vol.6, No.1, February 2014 DOI : 10.5121/ijma.2013.6101 1
  48. Vijaya Sathiaraj (2012) “A Study on the Neural Network Model for Finger Print Recognition” International Journal Of Computational Engineering Research ( Vol. 2 Issue. 5, Oct 2012
  49. Yan Y. and Zang Y.(2011) “ Multimodal Biometrics Fusion Using Correlation Filter Bank”, IEEE, DOI-978-1-4244-2175-6
  50. Zhang Q. and Zhang X.,(2010) “Research of Key Algorithm in the Technology of Fingerprint Identification,” Second IEEE International Conference on Computer Modeling and Simulation, pp. 282-284, 2010.
  51. Zhao W. et al, (2000) “Face recognition: a literature survey”, Technical Report CAR-TR-948, University of Maryland, October 2000.
  52. Zoran Bojkovic & Andreja Samcovic (2006) Face Detection Approach In Neural Network Based Method For Video Surveillance, 8th Seminar on Neural Network Applications in Electrical Engineering, Neurel, Faculty Of Electrical Eng., University Of Belgrade, Serbia, September 25-27.
Index Terms

Computer Science
Information Sciences


Multibiometric Authentication Enhanced Extracted Face (EEF) Plainarized Region of Interest (PROI) Preprocessing Recognition speed Cascaded Link Feed Forward Neural Network (CLFFNN) Back propagation.