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Integrated Biometric Verification System Using Soft Computing Approach

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Abstract

Among various biometric verification systems, fingerprint verification is one of the most reliable and widely accepted. One essential part of fingerprint verification is the minutiae extraction system. Most existing minutiae extraction methods require image preprocessing or post processing resulting in additional complex computation and time. Hence, direct gray-scale minutiae extraction approach on the image is preferred. One of these approaches is the use of Fuzzy Neural Network (FNN) as a recognition system to detect the presence of minutiae pattern. Currently, the development of FNN as a tool of recognition has shown a promising prospect. Some researchers have proposed several types of FNN. In particular, a Generic Self Organizing Fuzzy Neural Network (GENSOFNN) has been shown to excel in comparison with other FNN. Therefore, a new approach to perform direct gray-scale minutiae extraction based on GENSOFNN is proposed in this paper. Experimental results show the potential of using GENSOFNN for real-time point of sale (POS) terminal for verification.

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References

  1. Phillips S. (1999) Passwords could be past tense by 2002. Computers and Security 18(1): 75–76

    Google Scholar 

  2. Jain A., Hong L., Boole R. (1997) On-line fingerprint verification. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(4): 302–314

    Article  Google Scholar 

  3. Zhang J., Yan Y., Lades M. (1997) Face recognition: eigenface, elastic matching, and neural nets. Proceedings of the IEEE 85(9): 1423–1435

    Article  Google Scholar 

  4. Wahab A., Ng G.S., Dickiyanto R (2005) Speaker authentication system using soft computing approaches. Neurocomputing 68, 13–37

    Article  Google Scholar 

  5. http://ctl.ncsc.dni.us/biomet%20web/BMRetinal.html.

  6. Wildes R.P. (1997) Iris recognition: an emerging biometric technology. Proceedings of the IEEE 85(9): 1348–1363

    Article  Google Scholar 

  7. Sanchez-Reillo R., Gonzalez-Macros A. (2000) Access control system with hand geometry verification and smartcards. IEEE Aerospace and Electornic Systems Magazine 15(2): 45–48

    Article  Google Scholar 

  8. http://www.biometricgroup.com/reports/public/reports_signature-scan.html.

  9. Simon-Zorita D., Ortega-Garcia J., Cruz-Llanas S., Gonzalez-Rodriguez J. (2001) extraction scheme for fingerprint recognition systems. IEEE International Conference on Image Processing 3, 254–257

    Google Scholar 

  10. Maio D., Maltoni D. (1997) Direct gray-scale minutiae detection in fingerprints. IEEE Transaction on Pattern Analysis and Machine Intelligence. 19(1): 27–40

    Article  Google Scholar 

  11. Tico M., Immonen E., Ramo P., Kuosmanen P., Saarinen J. (2001) Fingerprint recognition using wavelet features. IEEE International Symposium on Circuits and Systems 2, 21–24

    Google Scholar 

  12. Maio D., Maltoni D., Cappelli R., Wayman J.L., Jain A.K. (2002) FVC2000: fingerprint verification competition. IEEE Transactions on Pattern Analysis and Machine 24(3): 402–412

    Article  Google Scholar 

  13. Quek C. and Wahab A.: Direct gray-scale minutiae extraction in fingerprint identification, In: Proceedings of the 13th International Conference on Parallel and Distributed system, pp. 646–651, Nevada, USA: Las Vegas, 2000

  14. Zhou R.W., Quek C. (1996) A pseudo outer-product based fuzzy neural network. Conference on Neural Networks 2, 957–962

    Google Scholar 

  15. Zadeh L., (1975) The calculus of fuzzy restrictions. In: Zadel L.A. et al. (eds) Fuzzy Sets and Applications to Cognitive and Decision Making Processes. Academic Press, New York, pp. 1–39

    Google Scholar 

  16. Ng G.S., Erdogan S.S., Ng P.W. (1995) Contender’s network, a new competitive-learning scheme. Pattern Recognition Letters 16(11): 1111–1118

    Article  Google Scholar 

  17. Ng G.S., Sim H.C. (1998) Recognition of partially occluded objects with back-propagation neural network. International Journal of Pattern Recognition and Artificial Intelligence 12(5): 645–660

    Article  Google Scholar 

  18. Tan T.Z., Quek C., Ng G.S. (2005) Ovarian cancer diagnosis by hippocampus and neocortex-inspired learning memory structures. Neural Networks 18(5–6): 818–825

    Article  Google Scholar 

  19. Ng G.S., Singh H. (1998) Data equalisation with evidence combination for pattern recognition. Pattern Recognition Letters 19(3–4): 227–235

    Article  MATH  Google Scholar 

  20. Tung W.L., Quek H.C. A novel approach to the derivation of fuzzy membership functions using the Falcon-MART architecture, Pattern Recognition Letters 22 (2000), 941–958

    Google Scholar 

  21. Lin C. J and Lin C.T. (1997), An ART-based fuzzy adaptive learning control network. IEEE Transaction on Fuzzy Systems 5(4): 477–496

    Article  Google Scholar 

  22. Tung W.L., Quek H.C. (2002) GenSoFNN: a generic self-organising fuzzy neural network. IEEE Transactions on Neural Network 13(5): 1075–1086

    Article  Google Scholar 

  23. Rumelhart D.E., Hinton G.E. and Williams R.J.: Learning internal representations by error propagation, In: D. E. Rumelhart, J. L. McClelland et al. (eds.), Parallel Distributed Processing, Cambridge, MA, USA: MIT Press, vol. 1, Chapter 8, 1986

  24. Carpenter G.A., Grossberg S. (1990) ART 3: hierarchical search using chemical transmitters in self-organizing pattern recognition architectures Carpenter. Neural Networks 3(2): 129–52

    Article  Google Scholar 

  25. Kohonen T. (1990) Improved versions of learning vector quantization. International Joint on Neural Networks 1, 545–550

    Article  Google Scholar 

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Authors and Affiliations

  1. Center for Computational Intelligence, School of Computer Engineering, Nanyang Technological University, Nanyang Drive, Singapore, 639798, Singapore

    A. Wahab, G. S. Ng & A. Jonatan

Authors
  1. A. Wahab
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  2. G. S. Ng
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  3. A. Jonatan
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Correspondence to G. S. Ng.

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Wahab, A., Ng, G.S. & Jonatan, A. Integrated Biometric Verification System Using Soft Computing Approach. Neural Process Lett 25, 111–126 (2007). https://doi.org/10.1007/s11063-006-9024-7

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  • DOI: https://doi.org/10.1007/s11063-006-9024-7

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