Skip to main content
SpringerLink
Log in

An effective segmentation method for iris recognition based on fuzzy logic using visible feature points

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

In this paper, the edge corners (ECs) are proposed as new visible feature points located at the edges of visible iris features such as crypts, pigment spots and stripes. A new technique is developed to segment the iris using the ECs. In addition, an efficient artificial intelligence based fuzzy logic system for the iris recognition stage is used to mitigate the randomness of the iris’s visible features due to pupil dilations and elastic distortions. Iris recognition is achieved by comparing the distribution pattern of the ECs using the proposed fuzzy logic system with four linguistic variables. The first goal is to achieve a high recognition rate with very low computational time. The second goal is to facilitate the use of iris recognition in forensics by using only ECs of the visible features of the iris rather than using full images of those features. Therefore, the proposed fuzzy logic based iris segmentation and recognition (FLISR) system can be used for automatic evaluation and manual verification. In the automatic evaluation, the system finds the best gallery iris image(s) matching the input probe image. Manual verification is done when trained examiners perform independent inspections to determine the best matching iris image. Extensive experiments with different data sets demonstrate the efficiency of the proposed FLISR. In terms of iris segmentation, the iris localization has reached an average accuracy of 99.85%. In addition, the average matching accuracy of the iris recognition has achieved 99.83% with very low computational time as compared to similar algorithms available in the literature.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14

Similar content being viewed by others

Availability of data and material

We ensure that a custom code is developed to support claims and comply with field standards.

Code availablilty

A custom code is available.

References

  1. Adler FH (1959) Physiology of the eye: Clinical applications, 3rd ed. C. V. Mosby Company, St. Louis

  2. Al-Waisy AS, Qahwaji R, Ipson S, Al-Fahdawi S (2018) A multi-biometric iris recognition system based on deep learning approach. Pattern Anal Applic 21(3):783–802

    Article  MathSciNet  Google Scholar 

  3. Arora S, Bhatia MP (2018) A Computer Vision System for Iris Recognition Based on Deep Learning. In: Proceedings of 2018 IEEE International Advance Computing Conference

  4. Barra S, Bisogni C, Nappi M, Ricciardi S (2019) F-FID: fast fuzzy-based iris de-noising for mobile security applications. Multimed Tools Appl 78(10):14045–14065

    Article  Google Scholar 

  5. Belcher C, Du Y (2009) Region-based SIFT approach to iris recognition. Opt Lasers Eng 47(1):139–147

    Article  Google Scholar 

  6. Boles W, Boashash B (1998) A human identification technique using images of the iris and wavelet transform. IEEE Trans Signal Process 46(4):1185–1188

    Article  Google Scholar 

  7. Chinese Academy of Sciences; Institute of Automation. Biometrics Ideal Test. CASIA iris Database (2015) Available online: http://biometrics.idealtest.org/

  8. Chen J, Shen F, Chen DZ, Flynn PJ (2016) Iris Recognition Based on Human-Interpretable Features. IEEE Trans Inf Forensics Secur 11(7):1476–1485

    Article  Google Scholar 

  9. Cui J, Wang Y, Tan T, Ma L, Sun Z (2004) An Iris Recognition Algorithm Using Local Extreme Points. In: Proc International Conference on Biometric Authentication, pp 442–449

  10. Darabkh K, AI-Zubi R, Jaludil M, AI-Kurdi H (2014) An Efficient Method for Feature Extraction of Human Iris Patterns, IEEE 11th International Multi-Conference on Systems, Signals & Devices (SSD14)

  11. Daugman J (1993) High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal Mach Intell 15(11):1148–1161

    Article  Google Scholar 

  12. Daugman J (2001) Statistical richness of visual phase information: update on recognizing persons by iris patterns. Int J Comput Vis 45(1):25–38

    Article  Google Scholar 

  13. Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video Technol 14:21–30

    Article  Google Scholar 

  14. Daugman J (2006) Probing the uniqueness and randomness of iris codes: ’ results from 200 billion iris pair comparisons’. Proc IEEE 94(11):1927–1935

    Article  Google Scholar 

  15. De Marsico M, Nappi M, Proença H (2017) Results from MICHE II - Mobile Iris CHallenge evaluation II. Pattern Recogn Lett 91(1):1–2

    Article  Google Scholar 

  16. Faundra M, Sulistyaningrum D (2017) Iris Segmentation and Normalization Algorithm Based on Zigzag Collarette. J Phys Conf Ser 795, conference 1:012049

    Article  Google Scholar 

  17. Freeman H, Davis LS (1997) In A Corner Finding Algorithm for Chain Coded Curves. IEEE Trans Comput 26:–303

  18. Garibaldi J, John R (2003) Choosing membership functions of linguistic terms. In: Proc 12th IEEE International Conference on Fuzzy Systems, vol. 1, pp. 578–583

  19. Happold M (2015) Structured forest edge detectors for improved eyelid and Iris segmentation. In: Proceedings of 2015 international conference of the biometrics special interest group, Darmstadt, Germany. pp. 28–33

  20. Harifi S, Bastanfard A (2015) Efficient Iris Segmentation Based on Converting Iris Images to High Dynamic Range Images, Second International Conference on Computing Technology and Information Management (ICCTIM)

  21. He F, Han Y, Wang H, Ji J, Liu Y, Ma Z (2017) Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. J Electron Imaging 26(2):023005

    Article  Google Scholar 

  22. Indian Institute of Technology Delhi, IIT Delhi Iris Database, see http://www4.comp.polyu.edu.hk/csajaykr/database.php

  23. Jain K, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20. https://doi.org/10.1109/TCSVT.2003.818349

    Article  Google Scholar 

  24. Joshi K, Agrawal S (2015) An Iris Recognition Based Robust Intrusion Detection System. In: Proc IEEE Annual India Conference, ISBN 4673–6540, pp. 1–6.

  25. Khalighi S (2015) Iris recognition using robust localization and nonsubsampled contourlet based features. J signal process Syst 81(1):111–128

    Article  Google Scholar 

  26. Kirsch R (1971) Computer determination of the constituent structure of biological images. Comput Biomed Res 4:315–328

    Article  Google Scholar 

  27. Li H, Sun Z, Tan T (2012) Robust Iris segmentation based on learned boundary detectors. In: Proceedings of 2012 5th IAPR international conference on biometrics, New Dehli, India. pp. 317–322

  28. Li C, Zhou W, Yuan S (2015) Iris recognition based on a novel variation of local binary pattern. Vis Comput 31(10):1419–1429

    Article  Google Scholar 

  29. Li Y, Po-Jen H, Huang, Juan Y (2019) An efficient and robust Iris segmentation algorithm using deep learning, Mobile Information Systems, pp 1–14, 2019

  30. Liu C, Wang J, Peng C, Shyu J (2014) Evaluating and selecting the biometrics in network security. Secur Commun Netw 8:727–739

    Article  Google Scholar 

  31. Ma L, Tan T, Wang Y, Zhang D (2003) Personal identification based on iris texture analysis. IEEE Trans Pattern Anal Mach Intell 25(12):1519–1533

    Article  Google Scholar 

  32. Maltoni D, Maio D, Jain AK, Prabhakar S (2009) Handbook of fingerprint recognition (2nd edition.). Springer Verlag. https://doi.org/10.1007/978-1-84882-254-2

  33. Mehrotra H, Sa PK, Majhi B (2013) Fast segmentation and adaptive SURF descriptor for iris recognition. Math Comput Model 58(1–2):132–146

    Article  Google Scholar 

  34. Minaee S, Abdolrashidi A (2019) DeepIris: Iris Recognition Using A Deep Learning Approach, ArXiv:1907.09380v1

  35. Nachar R, Inaty E, Bonnin P, Alayli Y (2019) Feature points using hybrid minutiae and corners for increased fingerprint recognition performance, Pattern Analysis and Applications - Springer

  36. Naderi H, Soleimani B, Araabi B and Zadeh H (2016) Fusing Iris, Palmprint and Fingerprint in a Multi-Biometric Recognition System. In: Proc IEEE International Conference on Computer and Robot vision, ISBN 5090–2491, pp. 327–334

  37. Proenca H, Filipe S, Santos R, Oliveira J, Alexandre LA (2010) The UBIRIS.v2: A database of visible wavelength iris image captured onthe-move and at-a-distance. IEEE TPAMI 32(8):1529C1535

    Google Scholar 

  38. Qiaoli G, Junsheng Z (2018) An Iris recognition algorithm for identity authentication, in proc international conference on intelligent transportation, Big Data & Smart City

  39. Radman A, Zainal N, Suandi SA (2017) Automated segmentation of iris images acquired in an unconstrained environment using hog-svm and growcut. Digit Signal Process 64:60–70

    Article  MathSciNet  Google Scholar 

  40. Ravi J, Raja KB, Venugopal KR (2009) Fingerprint recognition using minutiae score matching. Int J Eng Sci Technol 1(2):35–42

    Google Scholar 

  41. Roy D, Soni US (2016) Iris Segmentation Using Daughman’s Method. In: Proc International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

  42. Satish Adhau A, Shedge D (2015) Iris Recognition methods of a Blinked-Eye in Non-ideal Condition. In: Proc IEEE International conference on Information Processing, ISBN 4673–7758, pp. 75–79

  43. Shen F (2014) A visually interpretable iris recognition system with crypt features, Ph.D. dissertation, Dept. Comput. Sci. Eng., Univ. Notre Dame, Notre Dame, IN, USA

  44. Shen F, Flynn PJ (2012) Iris Matching by Crypts and Anti crypts. In: Proc IEEE Conference on Technologies for Homeland Security (HST)

  45. Shen F, Flynn PJ (2012) Using crypts as iris minutiae. In: Proc IEEE Conference on Technologies for Homeland Security (HST)

  46. Shen F, Flynn PJ (2013) Are iris crypts useful in identity recognition?, In: Proc IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS)

  47. Shen F, Flynn PJ (2014) Iris crypts: Multi-scale detection and shape based matching. In: Proc. IEEE Winter Conf. Appl. Comput. Vis, pp. 977–983

  48. Umer S, Dhara BC, Chanda B (2016) Texture code matrix-based multi-instance iris recognition. Pattern Anal Applic 19(1):283–295

    Article  MathSciNet  Google Scholar 

  49. Wang C, Zhu C, Liu Y, He R (2019) Joint Iris Segmentation and Localization Using Deep Multi-task Learning Framework, arXiv:1901.11195v2

  50. Wildes RP, Asmuth JC, Green GL, Hsu SC, Kolczynski RJ, Matey JR, McBride SE (1996) A machine-vision system for iris recognition. Mach Vis Appl 9:1–8

    Article  Google Scholar 

  51. Yang G, Pang S, Yin Y, Li Y, Li X (2013) SIFT based iris recognition with normalization and enhancement. Int J Mach Learn Cybern 4(4):401–407

    Article  Google Scholar 

  52. Yefrenes R, Martini D, Intiri G (2016) A Novel Approach for Iris Recognition, IEEE Region Symposium, ISBN 5090–0931, pp.231–236

  53. Zadeh L (1965) Fuzzy sets. Inf Control 8(3):338–353

    Article  Google Scholar 

  54. Zhao J, Bose BK (2002) Evaluation of membership functions for fuzzy logic controlled induction motor drive. In: Proc 28th Annual IEEE Conference of the Industrial Electronics Society, vol. 1, pp. 229–234

Download references

Funding

No funds, grants, or other support was received.

Author information

Authors and Affiliations

  1. Department of Telecommunications and Networks, University of Balamand, P.O. Box # 33, Elkoura, Lebanon

    Rabih Nachar

  2. Department of Computer Engineering, University of Balamand, P.O. Box # 33, Elkoura, Lebanon

    Elie Inaty

Authors
  1. Rabih Nachar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Elie Inaty
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rabih Nachar.

Ethics declarations

Conflicts of interest/competing interests

The authors have no conflicts of interest to declare that are relevant to the content of this article.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nachar, R., Inaty, E. An effective segmentation method for iris recognition based on fuzzy logic using visible feature points. Multimed Tools Appl 81, 9803–9828 (2022). https://doi.org/10.1007/s11042-022-12204-8

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-12204-8

Keywords

Search

Navigation