Neural network based biometric personal identification with fast iris segmentation

  • Rahib Hidayat Abiyev
  • Koray Altunkaya


This paper presents the iris recognition system for biometric personal identification using neural network. Personal identification consists of localization of the iris region and generation of a data set of iris images followed by iris pattern recognition. In this paper, a fast algorithm is proposed for the localization of the inner and outer boundaries of the iris region. Located iris is extracted from an eye image, and, after normalization and enhancement, it is represented by a data set. Using this data set a Neural Network (NN) is used for the classification of iris patterns. The adaptive learning strategy is applied for training of the NN. The results of simulations illustrate the effectiveness of the neural system in personal identification.


Biometric personal identification iris localization iris recognition neural network 


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  1. [1]
    A. Jain, R. Bolle, and S. PanKanti, eds., Biometrics: Personal Identification in a Networked Society, Kluwer, 1999.Google Scholar
  2. [2]
    F. Adler, Physiology of the Eye: Clinical Application, fourth ed., The C.V. MosbyCompany, London, 1965.Google Scholar
  3. [3]
    J. Daugman, Biometric Personal Identification System Based on Iris Analysis, United States Patent, no. 5291560, March 1994.Google Scholar
  4. [4]
    J. Daugman, “Statistical richness of visual phase information: Update on recognizing persons by iris patterns,” International Journal of Computer Vision, vol. 45, no. 1, pp. 25–38, 2001.zbMATHCrossRefGoogle Scholar
  5. [5]
    J. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition,” International Journal of Wavelets, Multiresolution and Information Processing, vol. 1, no. 1, pp. 1–17, 2003.zbMATHCrossRefGoogle Scholar
  6. [6]
    J. Daugman, “How iris recognition works,” University of Cambridge, 2001.Google Scholar
  7. [7]
    R. Wildes, J. Asmuth, G. Green, S. Hsu, R. Kolczynski, J. Matey, and S. McBride,“A machinevision system for iris recognition,” Machine Vision and Applications, vol. 9, pp. 1–8, 1996.CrossRefGoogle Scholar
  8. [8]
    R. Wildes, “Iris recognition: An emerging Biometric Technology,” Proc. of the IEEE, vol. 85, no. 9, pp. 1348–1363, September 1997.CrossRefGoogle Scholar
  9. [9]
    W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform,” IEEE Trans. on Signal Processing, vol. 46, no. 4, pp. 1185–1188, 1998.CrossRefGoogle Scholar
  10. [10]
    L. Masek, “Recognition of human iris patterns for biometric identification,” Technical Report, School of Computer Science and Soft Engineering, The University of Western Australia, 2003.Google Scholar
  11. [11]
    M. Y. Nam, X. Wang, and P. K. Rhee, “Efficient eye location for biomedical imaging using two-level classifier scheme,” International Journal of Control, Automation, and Systems, vol. 6, no. 6, pp. 828–835, 2008.Google Scholar
  12. [12]
    L. Ma, Y. H. Wang, and T. N. Tan, “Iris recognition based on multichannel Gabor filtering,” Proc. of the Fifth Asian Conference on Computer Vision, Australia, pp. 279–283, 2002.Google Scholar
  13. [13]
    L. Ma, T. Tan, Y. Wang, and D. Zhang, “Personal identication based on iris texture analysis,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 12, pp. 1519–1533, Dec. 2003.CrossRefGoogle Scholar
  14. [14]
    C. Tisse, L. Martin, L. Torres, and M. Robert. “Person identification technique using human iris recognition,” Proc. of Vision Interface, pp. 294–299, 2002.Google Scholar
  15. [15]
    H. Kanag and G. Xu, “Iris recognition system,” Journal of Curcuit and Systems, vol. 15, no. 1, pp. 11–15, 2000.Google Scholar
  16. [16]
    W. Yuan, Z. Lin, and L. Xu, “A rapid iris location method based on the structure of human eyes,” Proc. of the 27th IEEE Annual Conferemce Engineering in Medicine and Biology, Shanghai, China, September 1–4, 2005.Google Scholar
  17. [17]
    J. Daugman, and C. Downing, “Recognizing iris texture by phase demodulation,” IEEE Colloquium on Image Processing for Biometric Measurement, vol. 2, pp. 1–8, 1994.Google Scholar
  18. [18]
    K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi, and H. Nakajima, “An effective approach for iris recognition using phase-based image matching,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 30, no. 10, pp. 1741–1756, October 2008.CrossRefGoogle Scholar
  19. [19]
    C. Sanchez-Avila and R. Sanchez-Reillo, “Irisbased biometric recognition using dyadic wavelet transform,” IEEE Aerospace and Electronic Systems Magazine, vol. 17, no. 10, pp. 3–6, 2002.CrossRefGoogle Scholar
  20. [20]
    S. Noh, K. Bae, and J. Kim, “A novel method to extract features for iris recognition system,” Proc. the 4th Int. Conf. Audio-and Video-Based Biometric Person Authentication, pp. 838–844, 2003.Google Scholar
  21. [21]
    S. Mallat, “Zero-crossings of a wavelet transform,” IEEE Trans. Inf. Theory, vol. 37, no. 4, pp. 1019–1033, Apr. 1992.CrossRefMathSciNetGoogle Scholar
  22. [22]
    C. Park, J. Lee, M. Smith, and K. Park, “Iris-based personal authentication using a normalized directional energy feature,” Proc. of the 4th Int. Conf. Audio-and Video-Based Biometric Person Authentication, pp. 224–232, 2003.Google Scholar
  23. [23]
    S. Lim, K. Lee, O. Byeon, and T. Kim, “Efficient iris recognition through improvementof feature vector and classifier,” ETRI J., vol. 23, no. 2, pp. 61–70, 2001.Google Scholar
  24. [24]
    K. Bae, S. Noh, and J. Kim, “Iris feature extraction using independent component analysis,” Proc. the 4th Int. Conf. Audio- and Video-Based Biometric Person Authentication, pp. 838–844, 2003.Google Scholar
  25. [25]
    Y.-P. Huang, S.-W. Luo, and E. -Y. Chen, “An efficient iris recognition system,” Proc. of the First International Conference on Machine Learning and Cybernetics, Beijing, November, 2003.Google Scholar
  26. [26]
    Y. Wang and J.-Q. Han, “Iris recognition using independent component analysis,” Proc. of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005.Google Scholar
  27. [27]
    V. Dorairaj, N. Schmid, and G. Fahmy, “Performance evaluation of iris based recognition system implementing PCA and ICA techniques,” Proc. SPIE 2005 Symp, Orlando, 2005.Google Scholar
  28. [28]
    R. F. Larico Chavez, Y. Iano, and V. B. Sablon, “Process of recognition of human iris: Fast segmentation of iris,” Scholar
  29. [29]
    L. Ma, T. Tan, D. Zhang, and Y. Wang, “Local intensity variation analysis for iris recognition,” Pattern Recognition, vol. 37, no. 6, pp. 1287–1298, 2005.CrossRefGoogle Scholar
  30. [30]
    Z. Sun, Y. Wang, T. Tan, and J. Cui, “Improving iris recognition accuracy via cascaded classifiers,” IEEE Trans. on Systems, Mann, and Cybernetics-Part C: Applications and Reviews, vol. 35, no. 3, 2005.Google Scholar
  31. [31]
    F. Scotti, “Computational intelligence techniques for reflections identification in iris biometric images,” Proc. of IEEE International Conference on Computational Intelligence for Measurement Systems and Applications, Ostuni-Italy, 27–29 June 2007.Google Scholar
  32. [32]
    CASIA iris database. Institute of Automation, Chinese Academy of Sciences.

Copyright information

© The Institute of Control, Robotics and Systems Engineers and The Korean Institute of Electrical Engineers and Springer-Verlag Berlin Heidelberg GmbH 2009

Authors and Affiliations

  1. 1.Department of Computer EngineeringNear East UniversityLefkosaNorth Cyprus

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