Survey-Iris Recognition Using Machine Learning Technique

  • Padma NimbhoreEmail author
  • Pranali Lokhande
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 38)


In this digital era, Iris identification and detection are most useful and secure to use in banking, a financial section for security as well as it avoids fraud card detection. Iris recognition system gets images of an eyes by CSI scanner, after this, it traces out and senses the iris in the image which is then meant for the feature extraction, training, and matching. In this project, we will make use of two techniques by Iris image extraction for two separate classification method of the machine learning approach. Before feature extraction Normalization and Segmentation is used for the finding out the correct position of iris region in the particular portion of an eye with accuracy. This paper more focuses on machine learning approach to use supervised learning method.


Machine learn Biometrics Normalization Classification Hamming distance 


  1. 1.
    Roy, D.A., Soni, U.S.: Iris segmentation using Daughman’s method. In: IEEE ICEEOT (2016). ISBN 978-1-4673-9939-5Google Scholar
  2. 2.
    De Marsico, M., Petrosinob, A., Ricciard, S.: Iris recognition through machine learning techniques: a survey. Pattern Recogn. Lett. 82, 106–115 (2016)Google Scholar
  3. 3.
    Jung, Y., Kim, D., Son, B., Kim, J.: An eye detection method robust to eyeglasses for mobile iris recognition. Expert Syst. Appl. 67, 178–188 (2016)CrossRefGoogle Scholar
  4. 4.
    Daugman, J.: Searching for doppelgangers: assessing the universality of the Iris Code impostors distribution. IEEE IET J. 5(2) (2016). ISSN 2047 4946CrossRefGoogle Scholar
  5. 5.
    Gale, A.G., Salanka, S.S.: Evolution of performance analysis of iris recognition system by using a hybrid method of feature extraction and matching by the hybrid classifier for iris recognition system. In: IEEE ICEEOT (2016). ISBN 978-14673-9939-5Google Scholar
  6. 6.
    Abbdal, S.H., Kadhim, T.A., Abduljabbar, Z.A., Hussien, Z.A., et al.: Ensuring data integrity scheme based on digital signature and iris features in cloud. Indonesian J. Electr. Eng. Comput. Sci. (2016)Google Scholar
  7. 7.
    Nalla, P.R., KumaR, A.: Towards more accurate Iris recognition using cross spectral matching. IEEE (2016). ISBN 1057-7149Google Scholar
  8. 8.
    Nestorovic, N., Prasad, P.W.C., Alsadoon, A., Elchouemi, A.: Extracting unique personal identification number from iris. IEEE (2016). ISBN 978-1-5090-5398-8. School of Computing and Mathematics, Charles Sturt University, Sydney, Australia, Walden UniversityGoogle Scholar
  9. 9.
    Ali, H., Salami, M.: Iris recognition system using support vector machines. In: Riaz, Z. (ed.) Biometric Systems, Design, and Applications, pp. 169–182. In Tech 2011 (2008)Google Scholar
  10. 10.
    Roy, K., Bhattacharya, P.: Iris recognition with support vector machines. Advances in Biometrics, pp. 486–492. Springer, Heidelberg (2005)Google Scholar
  11. 11.
    Rai, H., Yadav, A.: Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst. Appl. 41, 588–593 (2014)CrossRefGoogle Scholar
  12. 12.
    Patil, S., Gudasalamani, S., Iyer, N.C.: A survey on iris recognition system. In: International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) – 2016 (2016). ISBN 978-14673-9939-5Google Scholar
  13. 13.
    Tan, C.-W., Kumar, A.: Towards online iris and periocular recognition under relaxed imaging constraints. IEEE Trans. Image Process. 22, 3751–3765 (2013). ISSN 1941-0042MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ibrahim, A.A., Khalaf, T.A., Ahmed, B.M.: Design and implementation of iris pattern recognition using wireless network system. J. Comput. Commun. (2016). ISSN 2327-5219Google Scholar
  15. 15.
    Chai, T.-Y., Goi, B.M., Tay, Y.H., Nyee, W.J.: A trainable method for iris recognition using random feature points. In: IEEE Conference (2017). ISBN 978-1-5386-4203-0Google Scholar
  16. 16.
    Daugman, J.: New methods in iris recognition. IEEE Trans. 37(5), 1167–1175 (2007). ISSN 1941-0492CrossRefGoogle Scholar
  17. 17.

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer Engineering and TechnologyMIT AOEPuneIndia

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