Skip to main content
SpringerLink
Log in

An Iris Recognition Approach with SIFT Descriptors

  • Conference paper
Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence (ICIC 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6839))

Included in the following conference series:

Abstract

In iris recognition systems how to represent texture pattern is an important issue. The paper proposes a novel approach based on SIFT for feature representation of iris texture. This approach partitions a normalized iris image into non-overlapping small sub-images and uses SIFT descriptor for representing the characteristics of each sub-image. As such the iris texture pattern is represented by an ordered-set of SIFT descriptors. This representation is very distinctive and insensitive to illumination changes. In addition, it encodes the positional information of iris texture pattern. For iris matching we use Bhattacharyya distance to measure the dissimilarity between two SIFT descriptors. The final distance is a sum of the distances of the corresponding pairs of SIFT descriptors in two iris images. The experimental results on UBIRIS.v1 and UBIRIS.v2 show that proposed method has promising performance.

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Belcher, C., Du, Y.: Region-based SIFT approach to iris recognition. Optics and Lasers in Engineering 47(1), 139–147 (2009)

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Bowyer, L.W., Hollingsworth, K., Flynn, P.J.: Image understanding for iris biometrics: a survey. Computer Vision and Image Understanding 110(2), 281–307 (2008)

    Article  Google Scholar 

  4. Comaniciu, D., Ramesh, V., Meer, P.: Kernel-based object tracking. IEEE Trans. Pattern Anal. Mach. Intell. 25(5), 564–575 (2003)

    Article  Google Scholar 

  5. Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell 15(11), 1148–1160 (1993)

    Article  Google Scholar 

  6. Li, P., Liu, X., Xiao, L., Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image and Vision Comput. 28(2), 246–253 (2010)

    Article  Google Scholar 

  7. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)

    Article  Google Scholar 

  8. Miyazawa, K., Aoki, T., Kobayashi, K., Nakajima, H.: An effective approach for iris recognition using phase-based image matching. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1741–1756 (2008)

    Article  Google Scholar 

  9. Monro, D.M., Soumyadip, R., Dexin, Z.: DCT-based iris recognition. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 586–595 (2007)

    Article  Google Scholar 

  10. NICE:II: Noisy iris challenge evaluation, part II (2010), http://nice2.di.ubi.pt/

  11. Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.: The UBIRIS.v2: A database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2010)

    Article  Google Scholar 

  12. Proenca, H., Luis, A.: Toward noncooperative iris recognition: A classification approach using multiple signatures. IEEE Trans. Pattern Anal. Mach. Intell. 29(4), 607–612 (2007)

    Article  Google Scholar 

  13. Proença, H., Alexandre, L.: UBIRIS: a noisy iris image database. In: Roli, F., Vitulano, S. (eds.) ICIAP 2005. LNCS, vol. 3617, pp. 970–977. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  15. Wildes, R.P.: Iris recognition:an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information and Electronics, Jia Mu Si University, China

    Xiaomin Liu

  2. School of Computer Science and Technology, Heilongjiang University, China

    Peihua Li

Authors
  1. Xiaomin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Peihua Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. School of Electronics and Information Engineering, Tongji University, 4800 Caoan Road, 201804, Shanghai, China

    De-Shuang Huang

  2. School of Computer and Communication Engineering, Zhengzhou University of Light Industry, No. 5, Dongfeng Road, Jinshui District, 450002, Zhengzhou, Henan, China

    Yong Gan

  3. Indian Institute of Technology Kanpur, 208016, Kanpur, India

    Phalguni Gupta

  4. Department of Biotechnology, Indian Institute of Technology Madras, 600 036, Chennai, Tamilnadu, India

    M. Michael Gromiha

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, X., Li, P. (2012). An Iris Recognition Approach with SIFT Descriptors. In: Huang, DS., Gan, Y., Gupta, P., Gromiha, M.M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence. ICIC 2011. Lecture Notes in Computer Science(), vol 6839. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25944-9_55

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-25944-9_55

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-25943-2

  • Online ISBN: 978-3-642-25944-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation