Skip to main content
SpringerLink
Log in

SIFT based iris recognition with normalization and enhancement

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

SIFT is a novel and promising method for iris recognition. However, some shortages exist in many related methods, such as difficulty of feature extraction, feature loss, and noise point introduction. In this paper, a new method named SIFT-based iris recognition with normalization and enhancement is proposed for achieving better performance. In Comparison with other SIFT-based iris recognition algorithms, the proposed method can overcome the difficulties of extreme point extraction and exclude the noise points without feature loss. Experimental results demonstrate that the normalization and enhancement steps are crucial for SIFT-based iris recognition, and the proposed method can achieve satisfactory recognition performance.

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

Similar content being viewed by others

References

  1. 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 

  2. 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  MATH  Google Scholar 

  3. 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 

  4. 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

    Article  Google Scholar 

  5. 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 

  6. Ma L, Tan T, Zhang D, Wang Y (2004) Local intensity variation analysis for iris recognition. Pattern Recognit 37(6):1287–1298

    Article  Google Scholar 

  7. Partio M, Cramariuc B, Gabbouj M (2004) Texture similarity evaluation using ordinal co-occurrence. In: Proceedings of the IEEE international conference on image processing, Singapore, pp 1537–1540

  8. Tisse C, Martin L, Torres L, Robert M (2002) Person identification technique using human iris recognition. In: Proceedings of the international conference on vision interface, pp 294–299

  9. Huang J, Ma L, Wang Y, Tan T (2004) Iris recognition based on local orientation description. In: Proceedings of the sixth Asian conference on computer vision, vol 2, Korea, pp 954–959

  10. Noh S, Bae K, Kim J (2003) A novel method to extract features for iris recognition system. In: Proceedings of the fourth international conference on audio- and video-based biometric person authentication, Guildford, pp 838–844

  11. Sanchez-Avila C, Sanchez-Reillo R (2005) Two different approaches for iris recognition using gabor filters and multiscale zero-crossing representation. Pattern Recognit 38(2):231–240

    Article  Google Scholar 

  12. Park C, Lee J, Smith M, Park K (2003) Iris-based personal authentication using a normalized directional energy feature. In: Proceedings of the fourth international conference on audio-and video-based biometric person authentication, Guildford, pp 224–232

  13. Lim S, Lee K, Byeon O, Kim T (2001) Efficient iris recognition through improvement of feature vector and classifier. ETRIJ 23(2):61–70

    Article  Google Scholar 

  14. Thornton J, Savvides M, Kumar V (2007) A bayesian approach to deformed pattern matching of iris images. IEEE Trans Pattern Anal Mach Intell 29(4):596–606

    Article  Google Scholar 

  15. Cui J, Wang Y, Tan T, Ma L, Sun Z (2004) An iris recognition algorithm using local extreme points. In: Proceedings of the first international conference on biometric authentication, Hong Kong, pp 442–449

  16. Monro DM, Rakshit S, Zhang D (2007) DCT-based iris recognition. IEEE Trans Pattern Anal Mach Intell 29(4):586–595

    Article  Google Scholar 

  17. Sun Z, Wang Y, Tan T, and Cui J (2004) Robust direction estimation of gradient vector field for iris recognition. In: Proceedings of the 17th international conference on pattern recognition, vol 2, Cambridge, pp 783–786

  18. Mojtaba N, Sedigheh G (2011) Iris recognition based on using ridgelet and curvelet transform. Int J Signal Process Image Process Pattern Recognit 4(2):7–18

    Google Scholar 

  19. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  20. Zhu R, Yang J, Wu R (2006) Iris recognition based on local feature point matching. In: International symposium on communications and information technologies, Bangkok, pp 451–454

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

    Article  Google Scholar 

  22. Wang L, Yang G, Yin Y (2010) Fast iris localization based on improved hough transform. In: Proceedings of RSKT’2010, Beijing, pp 439–446

  23. Xu X, Liu W, Venkatesh S (2011) An innovative face image enhancement based on principle component analysis. Int J Mach Learn Cybern. doi:10.1007/s13042-011-0060-x

    Google Scholar 

  24. Bruce P, Amin MA, Yan H (2011) Performance evaluation and comparison of PCA Based human face recognition methods for distorted images. Int J Mach Learn Cybern 2(4):245–259

    Article  Google Scholar 

  25. CASIA Iris Image Database. http://biometrics.idealtest.org

Download references

Acknowledgments

This work is supported by National Natural Science Foundation of China under Grant No. 61173069 and 61070097, and Shandong Province Higher Educational Science and Technology Program under Grant No. J11LG28. The authors would like to thank Wei Qin and Shuaiqiang Wang for their helpful comments and constructive advices on structuring the paper.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Shandong University, Jinan, 250101, People’s Republic of China

    Gongping Yang, Shaohua Pang, Yilong Yin, Yanan Li & Xuzhou Li

Authors
  1. Gongping Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shaohua Pang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yilong Yin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yanan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xuzhou Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yilong Yin.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Yang, G., Pang, S., Yin, Y. et al. SIFT based iris recognition with normalization and enhancement. Int. J. Mach. Learn. & Cyber. 4, 401–407 (2013). https://doi.org/10.1007/s13042-012-0101-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-012-0101-0

Keywords

Search

Navigation