Skip to main content
SpringerLink
Log in

Survey-Iris Recognition Using Machine Learning Technique

  • Conference paper
  • First Online:
Intelligent Data Communication Technologies and Internet of Things (ICICI 2019)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 38))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Roy, D.A., Soni, U.S.: Iris segmentation using Daughman’s method. In: IEEE ICEEOT (2016). ISBN 978-1-4673-9939-5

    Google Scholar 

  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. 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)

    Article  Google Scholar 

  4. Daugman, J.: Searching for doppelgangers: assessing the universality of the Iris Code impostors distribution. IEEE IET J. 5(2) (2016). ISSN 2047 4946

    Article  Google Scholar 

  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 978-14673-9939-5

    Google Scholar 

  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. Nalla, P.R., KumaR, A.: Towards more accurate Iris recognition using cross spectral matching. IEEE (2016). ISBN 1057-7149

    Google Scholar 

  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 University

    Google Scholar 

  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. Roy, K., Bhattacharya, P.: Iris recognition with support vector machines. Advances in Biometrics, pp. 486–492. Springer, Heidelberg (2005)

    Google Scholar 

  11. Rai, H., Yadav, A.: Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst. Appl. 41, 588–593 (2014)

    Article  Google Scholar 

  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 978-14673-9939-5

    Google Scholar 

  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 1941-0042

    Article  MathSciNet  Google Scholar 

  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 2327-5219

    Google Scholar 

  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 978-1-5386-4203-0

    Google Scholar 

  16. Daugman, J.: New methods in iris recognition. IEEE Trans. 37(5), 1167–1175 (2007). ISSN 1941-0492

    Article  Google Scholar 

  17. https://www.cogentsystems.com

Download references

Author information

Authors and Affiliations

  1. School of Computer Engineering and Technology, MIT AOE, Alandi, Pune, India

    Padma Nimbhore & Pranali Lokhande

Authors
  1. Padma Nimbhore
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pranali Lokhande
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Padma Nimbhore .

Editor information

Editors and Affiliations

  1. Department of ECE, Karunya University, Coimbatore, India

    D. Jude Hemanth

  2. Department of Electronics and Computer Engineering, Tribhuvan University, Kirtipur, Nepal

    Subarna Shakya

  3. School of Science, Edith Cowan University, Joondalup Campus, Joondalup, WA, Australia

    Zubair Baig

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nimbhore, P., Lokhande, P. (2020). Survey-Iris Recognition Using Machine Learning Technique. In: Hemanth, D., Shakya, S., Baig, Z. (eds) Intelligent Data Communication Technologies and Internet of Things. ICICI 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-34080-3_24

Download citation

Publish with us

Policies and ethics

Search

Navigation