Skip to main content
Log in

Human iris feature extraction under pupil size variation using local texture descriptors

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

Abstract

The human iris texture is one of the most reliable biometric traits because it is unique, and the iris pattern remains stable for years. However, iris images acquired under uncontrolled illumination is one source of difficulties for iris recognition systems, mainly in applications at a distance and in non-cooperative environments. Different levels of light cause iris texture modifications due to pupil size variation. The iris contains 02 groups of muscles: the sphincter pupillae and the dilator pupillae. When the sphincter pupillae contracts the iris reduces the size of the pupil and its texture changes. It is well known in the biometric literature that pupil dilation degrades iris biometric performance. We propose in this paper to evaluate some local texture descriptors for iris recognition, considering pupil contraction and dilation. Furthermore, we propose 02 new texture descriptors called Median-Local-Mapped-Pattern (Median-LMP) and Modified Median-Local-Mapped-Pattern (MM-LMP) and compare their performances to the original Local Mapped Pattern (LMP), the Completed Modeling of Local Binary Pattern (CLBP), the Median Binary Pattern (MBP), the Weber Local Descriptor (WLD) and the Daugman’s method. Our results show that our methodology is more robust when we compare iris samples with different levels of pupil sizes (dilated vs contracted). Besides this, our descriptor performs better than all the compared methods, primarily if one iris with a contracted pupil is used for searching another iris with a dilated pupil.

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

Access this article

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

Instant access to the full article PDF.

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
Fig. 15

Similar content being viewed by others

References

  1. Bashir F et al. (2008) Eagle-eyes: A system for iris recognition at a distance, 2008 IEEE Conference on Technologies for Homeland Security, Waltham, MA, USA, pp. 426 –431

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

    Article  Google Scholar 

  3. Cassin B, Solomon S (1990) Dictionary of Eye Terminology. Triad Publishing Company, Gainesville

    Google Scholar 

  4. Chen J, Shan S, He C, Zhao G, Pietikainen M, Chen X, Gao W (2010) WLD: A Robust Local Image Descriptor. IEEE Trans Pattern Anal Mach Intell 32(9):1705–1720. https://doi.org/10.1109/TPAMI.2009.155

    Article  Google Scholar 

  5. Chierici C et al. (2013) A new approach for analyzing rotated textures, IX WVC Workshop de Visão Computacional, Brazil. 6p., ISSN 2175-6120

  6. da Costa RM, Gonzaga A (2012) Dynamic Features for Iris Recognition. IEEE Trans Syst Man Cybern B Cybern 42(4):1072 -1082

  7. Daugman J (2002) How iris recognition works, in Proc. Int. Conf. Image Process. 1:I-33–I-36

  8. Dong W, Sun Z, Tan T (2009) A design of iris recognition system at a distance, IEEE Chinese Conf. on Pattern Recognition, Nanjing, China. 2:1 –5

  9. Doyle JS, Bowyer KW, Flynn PJ, Variation in accuracy of textured contact lens detection based on sensor and lens pattern, IEEE Sixth International Conference on Biometrics: Theory, Applications and Systems (BTAS). pp.1-7

  10. Fancourt C, Bogoni L, Hanna K, Guo Y, Wildes R, Takahashi N, Jain U (2005) Iris recognition at a distance, International Conference on Audio- and Video-Based Biometric Person Authentication. 1–13

  11. Ferraz CT, Junior OP, Rosa MV, Gonzaga A (2014) Object recognition based on bag of features and a new local pattern descriptor, International Journal of Pattern Recognition and Artificial Intelligence, World Scientific Publishing Company, vol 28(08), 32 pages. https://doi.org/10.1142/S0218001414550106

  12. Guo Z, Zhang D, Zhang D (2010) A Completed Modeling of Local Binary Pattern Operator for Texture Classification. IEEE Trans Image Process 19(6):1657–1663. https://doi.org/10.1109/TIP.2010.2044957

    Article  MathSciNet  MATH  Google Scholar 

  13. Hafiane A, Seetharaman G, and Zavidovique B (2007) Median binary pattern for texture classification, Proceedings of the 4th International Conference on Image Analysis and Recognition (Berlin, Heidelberg), ICIAR'07, Springer-Verlag, pp. 387-398

  14. Hanna K, Mandelbaum R, Mishra D, Paragano V, Wixson L (1996) A system for non-intrusive human iris acquisition and identification, IAPR Workshop on Machine Vision Applications. 200–203

  15. He Y, Cui J, Tan T, Wang Y (2006) Key techniques and methods for imaging iris in focus, International Conference on Pattern Recognition. 557–561

  16. Hollingsworth K, Bowyer KW, Flynn PJ (2009) Pupil dilation degrades iris biometric performance. Comput Vis Image Underst 113:150–157

    Article  Google Scholar 

  17. Institute of Automation, Chinese Academy of Sciences, CASIA Iris Image Database, CASIA-IrisV4, http://biometrics.idealtest.org/

  18. JAI – AD 080GE, http://www.jai.com/en/products/ad-080ge

  19. LAVI Iris Video DB2 - http://imagem.sel.eesc.usp.br/iris/

  20. Liu Y, Nie L, Han L, Zhang L, Rosenblum DS (2015) Action2activity: Recognizing complex activities from sensor data. IJCAI

  21. Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: Sensor-based activity recognition. Neurocomputing 181:108–115 Big Data Driven Intelligent Transportation Systems

    Article  Google Scholar 

  22. Lu Y, Wei Y, Liu L et al (2016) Towards unsupervised physical activity recognition using smartphone accelerometers. Multimed Tools Appl:1–19

  23. Martin A, Doddington G, Kamm T, Ordowski M, and Przybocki M (1997) “The DET curve in assessment of detection task performance,” in Proc. EUROSPEECH, pp. 1895–1898

  24. Matey JR, Naroditsky O, Hanna K, Kolczynski R, LoIacono D, Mangru S, Tinker M, Zappia T, Zhao WY (2006) Iris on the Move: acquisition of images for iris recognition in less constrained environments, Proc. IEEE. 94(11):1936–1946

  25. Nappi M, De Marsico M, Riccio D (2012) Noisy Iris Recognition Integrated Scheme. Pattern Recogn Lett 33:1006–1011

    Article  Google Scholar 

  26. Negri TT, Gonzaga A (2014) Color Texture Classification under Varying Illumination, X Workshop de Visão Computacional - WVC 2014, Uberlândia, MG, Brazil. pp. 61-66. https://doi.org/10.13140/2.1.1690.3684

  27. Ng TW, Tay TL, Khor SW (2010) Iris recognition using rapid Haar wavelet decomposition, 2nd International Conference on Signal Processing Systems (ICSPS). v.1, pp. V1-820-V1-823, 5-7

  28. Nguyen K et al (2017) Long range iris recognition: A survey. Pattern Recogn 72:123–143

    Article  Google Scholar 

  29. Ojala T, Pietikäinen M, Harwood D (1996) A comparative study of texture measures with classification based on featured distributions. Pattern Recogn 29(1):51–59

    Article  Google Scholar 

  30. Proenca H (2008) Iris Recognition: A Method to Segment Visible Wavelength Iris Images Acquired On-the-Move and At-a-Distance, Proceedings Advances in Visual Computing, Pt I, v. 5358. In: Bebis G (ed). Springer-Verlag Berlin, Berlin, pp. 731-742

  31. Proenca H (2009) On the feasibility of the visible wavelength, at-a-distance and on-the-move iris recognition, IEEE Workshop on Computational Intelligence in Biometrics: Theory, Algorithms, and Applications, Hangzhou, China. pp. 9-15

  32. Proenca H (2011) Quality Assessment of Degraded Iris Images Acquired in the Visible Wavelength. IEEE Trans Inform Forensics Secur 6:82–95

    Article  Google Scholar 

  33. Proença H, Alexandre LA (2005) Ubiris: A noisy iris image database, Proceed. of ICIAP 2005 - Intern. Confer. on Image Analysis and Processing. 1: 970-977

  34. Proença H, Neves JC (2018) Deep-PRWIS: periocular recognition without the iris and sclera using deep learning frameworks. IEEE Trans Inform Forensics Secur 13(4):888–896

    Article  Google Scholar 

  35. Radu P, Sirlantzis K, Howells G, Hoque S, Deravi F (2013) A Colour Iris Recognition System Employing Multiple Classifier Techniques. Electron Lett Comput Vis Image Anal 12(2):54–65

    Article  Google Scholar 

  36. Shams MY, Rashad MZ, Nomir O, El-Awady RM (2011) Iris recognition based on LBP and combined LVQ classifier. Int J Comput Sci Inform Technol (IJCSIT) 3(5):67–78

    Article  Google Scholar 

  37. Sun Z, Tan T (Dec. 2009) Ordinal measures for iris recognition. IEEE Trans Pattern Anal Mach Intell 31(12):2211–2226

    Article  Google Scholar 

  38. Vieira RT, Langoni VM, Gonzaga A (2014) Video-based Iris Recognition by Quasi-Dynamic Texture Analysis, X Workshop de Visão Computacional - WVC 2014, pp. 79-84. https://doi.org/10.13140/2.1.3263.2326

  39. Villar JD, Ives R, Matey J (2010) Design and implementation of a long range iris recognition system, Conference on Signals, Systems and Computers (ASILOMAR), 2010, Conference Record of the Forty Fourth Asilomar. [S.l.: s.n.], pp. 1770–1773. ISSN 1058-6393

  40. Viola P, Jones MJ (2004) Robust real-time face detection. Int. J. Comput. Vision, 2004. Kluwer Academic Publishers, Hingham, MA, USA, v. 57, n. 2, p. 137–154. ISSN 0920-5691

  41. Wildes R et al. (1994) A system for automated iris recognition, Proceedings of the Second IEEE Workshop on Applications of Computer Vision, [S.l.:s.n.], pp. 121–128

  42. Yang X, Liu W, Tao D, Cheng J (2017) Canonical correlation analysis networks for two-view image recognition. Inf Sci 385–386:338–352

    Article  Google Scholar 

  43. Zhao Z, Kumar A (2018) Improving periocular recognition by explicit attention to critical regions in deep neural network. IEEE Trans Inform Forensics Secur 13(12):2937–2952

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the Sao Paulo Research Foundation (FAPESP), grant #2015/20812-5.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Adilson Gonzaga.

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

de Souza, J.M., Gonzaga, A. Human iris feature extraction under pupil size variation using local texture descriptors. Multimed Tools Appl 78, 20557–20584 (2019). https://doi.org/10.1007/s11042-019-7371-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-019-7371-4

Keywords

Navigation