Skip to main content
Log in

Toward accurate localization and high recognition performance for noisy iris images

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

Abstract

Iris recognition plays an important role in biometrics. Until now, many scholars have made different efforts in this field. However, the recognition performances of most proposed methods degrade dramatically when the image contains some noise, which inevitably occurs during image acquisition such as reflection spots, inconsistent illumination, eyelid, eyelash, hair, etc. In this paper, an accurate iris localization and high recognition performance approach for noisy iris images is presented. After filling the reflection spots using the inpainting method which is based on Navier-Stokes (NS) equations, the Probable boundary (Pb) edge detection operator is used to detect pupil edge initially, which can eliminate the interference of inconsistent illumination, eyelid, eyelash and hair. Besides, the accurate circle parameters are obtained in delicately to reduce the input space of Hough transforms. The iris feature code is constructed based on 1D Log-Gabor filter. Our thorough experimental results on the challenging iris image database CASIA-Iris-Thousand achieve an EER of 1.8272 %, which outperforms the state-of-the-art methods.

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.

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
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. CASIA-Iris-Thousand, http://biometrics.idealtest.org/dbDetailForUser.do?id=4

  2. Bertalmio M, Bertozzi AL, Sapiro G, Stokes N (2001) Fluid dynamics, and image and video inpainting. In: Proc int conf comput vision pattern recognit, vol 1, pp 355–362

  3. Cho D, Park K, Rhee D (2005) Real-time iris localization for iris recognition in cellular phone. In: Proc int conf software eng, artif intelligence, networking parallel/dist comp first ACIS int workshop self-assem wireless networks, pp 254–259

  4. Criminisi A, Perez P, Toyama K (2004) Region filling and object removal by exemplar-based inpainting. IEEE Trans Image Process 13(9):1200–1212

    Article  Google Scholar 

  5. Daugman J (2000) Biometric decision landscapes. Cambridge University Comput Lab Tech Rep (482)

  6. Daugman J (2004) How iris recognition works. IEEE Trans Circuits Syst Video 14(1):21–30

    Article  Google Scholar 

  7. Dong W, Sun Z, Tan T (2011) Iris matching based on personalized weight map. IEEE Trans Pattern Anal Mach Intell 33(9):1744–1757

    Article  Google Scholar 

  8. Duda RO, Hart PE (1979) Use of the hough transformation to detect lines and curves in pictures. Commun ACM 15(1):11–15

    Article  Google Scholar 

  9. Feng J, Jain AK (2011) Fingerprint reconstruction: from minutiae to phase. IEEE Trans Pattern Anal Mach Intell 33(2):209–223

    Article  Google Scholar 

  10. Feng X, Fang C, Ding X, Wu Y (2006) Iris localization with dual coarse-to-fine strategy. In: Proc int conf pattern recognit, vol 4, pp 553–556

  11. Field D (1987) Relations between the statistics of natural images and the response properties of cortical cells. J Opt Soc Am 4(12):2379–2394

    Article  Google Scholar 

  12. Fogel I, Sagi D (1989) Gabor filters as texture discriminator. Biol Cybern 61(2):103–113

    Article  Google Scholar 

  13. Galbally J, Fierrez J, Ortega-Garcia J, McCool C, Marcel S (2009) Hill-climbing attack to an eigenface-based face verification system. In: Int conf Biom, Identity and Security (BIdS), pp 1–6

  14. He Z, Sun Z, Tan T, Qiu X, Zhong C, Dong W (2008) Boosting ordinal features for accurate and fast iris recognition. In: Proc IEEE int conf comput vision pattern recognit, pp 1–8

  15. He Z, Tan T, Sun Z, Qiu X (2009) Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans Pattern Anal Mach Intell 31(9):1670–1684

    Article  Google Scholar 

  16. Heeger D, Bergen J (1995) Pyramid-based texture analysis/synthesis. In: Proc Siggraph

  17. Hollingsworth KP, Bowyer KW, Flynn PJ (2011) Improved iris recognition through fusion of hamming distance and fragile bit distance. IEEE Trans Pattern Anal Mach Intell 33(12):2465–2476

    Article  Google Scholar 

  18. Jain AK, Flynn P, Ross AA (2008) Handbook of Biometrics, chap. Introduction to Biometrics. Springer, NJ, USA, pp 1–22

    Google Scholar 

  19. Liu X, Bowyer K, Flynn P (2005) Experiments with an improved iris segmentation algorithm. In: Proc IEEE workshop autom identif adv technol, pp 118–123

  20. Martin D, Fowlkes C, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530–549

    Article  Google Scholar 

  21. Puzicha J, Hofmann T, Buhmann J (1997) Non-parametric similarity measures for unsupervised texture segmentation and image retrieval. In: Proc IEEE int conf comput vision pattern recognit, pp 267–272

  22. Puzicha J, Rubner Y, Tomasi C, Buhmann J (1999) Empirical evaluation of dissimilarity measures for color and texture. In: Proc int conf comput vision, pp 1165–1172

  23. Roth S, Black MJ (2009) Fields of experts: a framework for learning image priors. Int J Comput Vision 82(2):205–229

    Article  Google Scholar 

  24. Scotti F (2007) Computational intelligence techniques for reflections identification in iris biometric images. In: IEEE int conf comput intelligence meas syst appl, pp 84–88

  25. Sheela SV, Vijaya PA (2010) Iris recognition methods-survey. Int J Comput Appl 3(5):19–25

    Google Scholar 

  26. Shen W, Surette M, Khanna R (1997) Evaluation of automated biometrics-based identification and verification systems. In: Proc IEEE, vol 85, pp 1464–1478

  27. Tisse C, Martin L, Torres L, Robert M (2002) Person identification technique using human iris recognition. In: Proc int conf vision interface, pp 294–299

  28. Trucco E, Razeto M (2005) Robust iris localization in close-up images of the eye. Pattern Anal Appl 8(3):247–255

    Article  MathSciNet  Google Scholar 

  29. Uhl A, Wild P (2012) Multi-stage visible wavelength and near infrared iris segmentation framework. Lect Notes Comput Sci 7352(PART 2):1–10

    Article  Google Scholar 

  30. Wang G, Wu H (2009) Research and realization on voice restoration technique for voice communication software. In: Int symp inf eng electron commerce, pp 791–795

  31. Wildes RP (1997) Iris recognition: an emerging biometric technology. Proc IEEE 85(9):1348–1365

    Article  Google Scholar 

  32. Yahya AE, Nordin MJ (2010) Improving iris segmentation by specular reflections removable. In: Int symp inf technol, pp 1–3

Download references

Acknowledgements

The authors would like to thank the reviewers for their valuable comments which are greatly helpful to improve the clarity and quality of this work. This work is supported by the National Natural Science Foundation of China (Grant Number: 60832010, 61100187) and the Fundamental Research Funds for the Central Universities (Grant Number: HIT. NSRIF. 2010046, HIT. NSRIF. 2013061).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiong Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Wang, N., Li, Q., Abd El-Latif, A.A. et al. Toward accurate localization and high recognition performance for noisy iris images. Multimed Tools Appl 71, 1411–1430 (2014). https://doi.org/10.1007/s11042-012-1278-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-012-1278-7

Keywords

Navigation