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Modified Circular Fuzzy Segmentor and Local Circular Encoder to Iris Segmentation and Recognition


Emmanvel Raj Manohar Chirchi1*, Kharadkar Ravindra Digambar2


1Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, India
2Gyanchand Hirachand Raisoni Institute of Engineering & Technology, Pune, India


Currently a lot of biometric procedures are being developed based on different features and algorithms. Nevertheless, it is known that, from all of these techniques, iris recognition is one of the most promising for high security applications. In this paper, a novel scheme is proposed to iris segmentation and recognition in iris based biometric system. In the new scheme, we use the modified circular fuzzy segmentor (MCFS) model to segment the pupil and iris inner boundary. After that, a binary encoder based feature extraction scheme named as LCE is proposed to extract the significant features to do the iris recognition process. Once feature extraction scheme is done by the LCE operator, the iris recognition is done through fuzzy logic classifier. We use three datasets from widely used iris databases (CASIA, MMU and UBIRIS) to analyze the increase of the error rates when the iris is inaccurately segmented. We selected 780 images of the CASIA, MMU and UBIRIS databases that the used segmentation algorithm can accurately segment. From the experimentation results, the proposed method of MCFS+LCE is outperformed than the existing methods.


Modified circular fuzzy segmentor, Fuzzy logic classifier, Local circular operator, CASIA, MMU, UBIRIS.

Full Text:

  1. E. Lupu and P. G. Pop, “Multimodal Biometric Systems Overview”, Acta Technica Napocensis, Electronics and Telecommunications, Vol. 49, No. 3, 2008.
  2. K. Jain, R. Bolle and S. Pankanti, eds., “Biometrics: Personal Identification in Networked Society”, Kluwer Academic Publishers, Vol. 479, 1999.
  3. D. Zhang, “Biometrics Technologies and Applications”, Proc. of International Conference on Image and Graphics, Tianjing, China, pp. 249-254, 2000.
  4. G. Lawton, “Biometrics: A New Era in Security”, IEEE Computer, pp. 16-18, Aug. 1998.
  5. H. Zhuang, T. Theerawong, X. Guan and S. Morgera, “A Method for Creating 3D Face from a 2D Face Image”, Florida Conference on Recent Advances in Robotics (FCRAR 2006), Miami, Florida, May, 2006.
  6. V. S. Meenakshi and G. Padmavathi, “Securing Iris Templates using Combined User and Soft Biometric based Password Hardened Fuzzy Vault”, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, No. 2, 2010.
  7. M. Baca and K. Rabuzin, “Biometrics in Network Security”, The Proceedings of the XXVIII International Convention MIPRO 2005, Rijeka, pp. 205-210, 2005.
  8. K. Jain, A. Ross and S. Prabhakar, “An introduction to biometric recognition”, IEEE Trans. on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 4-20, 2004.
  9. G. Chellin, J. Chandran and R. S. Rajesh, “Performance Analysis of Multimodal Biometric System Authentication”, IJCSNS International Journal of Computer Science and Network Security, Vol. 9, No. 3, 2009.
  10. P. Manikandan and M. Sundararajan, “Discrete Wavelet Features Extraction for Iris Recognition Based Bio Metric Security”, International Journal of Electronic Engineering Research, Vol. 2, No. 2, pp. 237-241, 2010.
  11. C. Y. Yam, M. S. Nixon and J. N. Carter, “Gait Recognition by Walking and Running: A Model-Based Approach”, ACCV 2002: The 5th Asian Conference on Computer Vision, pp. 1-6, 2002.
  12. L. Ma, T. Tan, Y. Wang and D. Zhang, “Personal Recognition Based on Iris Texture Analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 12, pp. 1519-1533, 2003.
  13. A. M. Sarhan, “Iris Recognition Using Discrete Cosine Transform and Artificial Neural Networks”, Journal of Computer Science, Vol. 5, No. 5, pp. 369-373, 2009.
  14. S. Dey and D. Samanta, “Improved Feature Processing for Iris Biometric Authentication System”, International Journal of Computer Systems Science and Engineering (IJCSSE), World Academy of Science, Vol. 4, No. 2, pp. 127-134, 2008.
  15. S. Dey and D. Samanta, “A Novel Approach To Iris Localization For Iris Biometric Processing”, International Journal of Biological and Life Sciences, Vol. 3, No. 3, pp. 180-191, 2007.
  16. W. Boles and B. Boashash, “A human identification technique using images of the iris and wavelet transform”, IEEE Transaction of Signal Processing, Vol. 46, No. 4, pp. 1185-1188, 1998.
  17. S. Lim, K. Lee, O. Byeon and T. Kim, “Efficient iris recognition through improvement of feature vector and classifier”, Journal of Electrical Technical Research Institute, Vol. 23, No. 2, pp. 61-70, 2001.
  18. S. P. Narote, A. S. Narote and L. M. Waghmare, “Iris Based Recognition System using Wavelet Transform”, International Journal of Computer Science and Network Security, Vol. 9, No. 11, pp. 101, November 2009.
  19. N. P. Bodorin, “Circular Fast Fuzzy Iris Segmentation & Fast k-Means Quantization Demo Programs”, June 2009,
  20. N. P. Bodorin, “Fast k-Means Image Quantization algorithm and its application to iris segmentation”, Scientific Bulletin, University of Pitesti, No. 14, 2008.
  21. J. Daugman, “How Iris Recognition Works”, In Proceedings of International Conference on Image Processing, Vol.1, pp. I-33- I-36, 2002.
  22. S. Chiu, “Fuzzy model identification based on cluster estimation,” Journal of Intelligent & Fuzzy Systems, Vol. 2, No. 3, pp. 267-278, 1994.
  23. H. Proença and L. A. Alexandre, “Ubiris: a noisy iris image database”, In International Conference on Image Analysis and Processing, Vol. 3617, pp. 970-977, 2005.
  24. J. Horst, “Iris Recognition: A General Overview”, Journal of Student Research, pp. 19-23, 2006.
  25. S. Uma Maheswari, P. Anbalagan and T. Priya, “Efficient Iris Recognition through Improvement in Iris Segmentation Algorithm”, International Journal on Graphics, Vision and Image Processing, Vol. 8, No. 2, pp. 29-35, 2008.
  26. N. P. Bodorin, “Circular Fast Fuzzy Iris Segmentation”, Department of Mathematics and Computer Science, Spiru Haret University, 2009.
  27. K. Miyazawa, K. Ito, T. Aoki, K. Kobayashi and H. Nakajima, “An Effective Approach for Iris Recognition Using Phase-Based Image Matching”, IEEE transactions on pattern analysis and machine intelligence, Vol. 30, No. 10, pp. 1741-1756, 2008.
  28. H. Mehrotra, P. K. Sa and B. Majhi, “Fast segmentation and adaptive SURF descriptor for iris recognition”, Mathematical and Computer Modelling, Vol. 58, No. 1, pp. 132-146, 2013.

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