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First International Conference on Artificial Intelligence and Cognitive Computing pp 27–38Cite as

Morphological-Based Localization of an Iris Image

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 815))

Abstract

Iris recognition is one of the reliable biometric techniques used for human identification purpose. It provides the unique information about a person with natural features such as both the left and right eye irises of a person is different and stable with the age and also the quality of the iris is not affected by contact lenses and eyeglasses. The authors suggested that iris recognition fails due to the tedious process involved during localization. The failure rate can be decreased by performing edge detection with a suitable localization algorithm. The authors proved that histogram equalization is one of the best image enhancement techniques to process an image with probability density function of different gray-level values. The edges of an image are identified using an edge detection algorithm using mean value and threshold values, and the localization of an image is rectified by the neighbors of a pixel and structuring element morphological operations. Compare the performance of the algorithms and prove that the localization of an edge using the structuring element of the morphological operation produces the best results compared with other morphological operations using the neighbors of a pixel.

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References

  1. John Daugman, ”Information Theory and Iris Codes”, 2016, IEEE Transactions on Information Forensics and Security, Volume 11, No 2, February.

    Google Scholar 

  2. John Daugman and Cathryn Downing, 2001, “Epigenetic Randomness, Complexity and Singularity of human iris patterns”, The Royal Society.

    Google Scholar 

  3. John Canny, 1986, “Computational approach to edge detection. Pattern Analysis and Machine Intelligence”, IEEE Transactions: 679–698.

    Google Scholar 

  4. Geng Xin, Chen Ke, Hu Xiaoguang, 2012, “An improved Canny edge detection algorithm for color image”, IEEE.

    Google Scholar 

  5. John Daugman and Cathryn Downing, April 2001,“ Epigenetic randomness, complexity and singularity of human iris patterns”, The Royal Society.

    Google Scholar 

  6. Han S. Ali, Asmaa I. Ismail, Fathi. A. Farag, 2016 “Speedup robust features for efficient iris recognition” Springer-Verlag, London 2016, SIViP 10:1385–1391, https://doi.org/10.1007/s11760-016-0903-8.

    Article  Google Scholar 

  7. John Daugman, 2004, “How Iris Recognition works”, IEEE transactions on Circuits and systems for video technology, Vol 14, No 1.

    Google Scholar 

  8. Izem Hamouchene, Saliha Aouat, 2016, “Efficient approach for iris recognition”, Springer-Verlag, London, https://doi.org/10.1007/s11760-016-0900-y.

    Article  Google Scholar 

  9. Chun- Chih Tsai, Heng –Yi Lin, Jinshiuh Taur, 2012, “Iris Recognition Using Possibilistic Fuzzy Matching on Local Featues”, IEEE Transactions on Systems, Man and Cybernetics, Vol 42, No. 1.

    Google Scholar 

  10. John Daugman, 2003, “The importance of being random: statistical principles of iris recognition”, Pattern Recognition 36 279 – 291.

    Article  Google Scholar 

  11. Hao F, Daugman J, Zielinski P, June 2008, “A fast search algorithm for a large fuzzy database.” IEEE Trans. Information Forensics and Security, Volume 3 and No 2, pp 203–212.

    Google Scholar 

  12. Daugman J, October 2007, “New methods in iris recognition.” IEEE Trans. Systems, Man, Cybernetics, part- B volume 37 and No 5,   pp 1167–1175.

    Google Scholar 

  13. Vanaja Roselin E. Chirchi and L. M. Waghmare, Research Scholar, JNTUH, Kukatpally Hyderabad-500085(AP), India, 2013, “Feature Extraction and Pupil Detection Algorithm Used for Iris Biometric Authentication System”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 6, No. 6, pp 141–160, http://dx.doi.org/10.14257/ijsip.2013.6.6.14.

    Article  Google Scholar 

  14. Eun-suk Cho, Ronnie D. Caytiles, Seok-soo Kim, 2011, “New Algorithm Biometric-Based Iris Pattern Recognition System: Basis of Identity Authentication and Verification”, (Journal of Security Engineering).

    Google Scholar 

  15. Radhika Chandwadkar, Saurabh Dhole, Vaibhav Gadewar, Deepika Raut, Prof. S. A. Tiwaskar ,October 2013, ” Comparison of Edge Detection Techniques”, Proceedings of Sixth IRAJ International Conference, Pune, India. http://dx.doi.org/10.13140/RG.2.1.5036.7123.

  16. Mahmoud Mahlouji and Ali Noruzi, January 2012, “Human Iris Segmentation for Iris Recognition in Unconstrained Environments”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3,  ISSN (Online): 1694-0814.

    Google Scholar 

  17. Prateek Verma, Maheedhar Dubey, Praveen Verma, Somak Basu, June 2012, ”Daughmans Algorithm Method For Iris Recognition-A Biometric Approach”, International Journal of Emerging Technology and Advanced Engineering, Volume 2, Issue 6. 

    Google Scholar 

  18. Lenina Birgale and M. Kokare, 2010, “Iris Recognition Without Iris Normalization”, Journal of Computer Science 6 (9): 1042–1047, 2010 ISSN 1549-3636, Science Publications.

    Google Scholar 

  19. Sajida Kalsoom and Sheikh Ziauddin, 2012 “Iris Recognition: Existing Methods and Open Issues”, PATTERNS: The Fourth International Conferences on Pervasive Patterns and Applications.

    Google Scholar 

  20. Zhuoshi Wei, Xianchao Qiu, Zhenan Sun and Tieniu Tan, 2008, “Counterfeit Iris Detection Based on Texture Analysis”, 978-1-4244-2175-6/08/$25.00, IEEE.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Hindustan Institute of Technology and Science, Chennai, India

    S. G. Gino Sophia

  2. Department of Information Technology, Hindustan Institute of Technology and Science, Chennai, India

    V. Ceronmani Sharmila

Authors
  1. S. G. Gino Sophia
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  2. V. Ceronmani Sharmila
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Corresponding author

Correspondence to S. G. Gino Sophia .

Editor information

Editors and Affiliations

  1. School of Computer and Information Sciences, University of Hyderabad, Hyderabad, Telangana, India

    Raju Surampudi Bapi

  2. Department Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India

    Koppula Srinivas Rao

  3. IDRBT, Hyderabad, Telangana, India

    Munaga V. N. K. Prasad

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© 2019 Springer Nature Singapore Pte Ltd.

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Cite this paper

Gino Sophia, S.G., Ceronmani Sharmila, V. (2019). Morphological-Based Localization of an Iris Image. In: Bapi, R., Rao, K., Prasad, M. (eds) First International Conference on Artificial Intelligence and Cognitive Computing . Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-13-1580-0_3

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