Skip to main content
SpringerLink
Log in

Efficient Iris Recognition System Using Relational Measures

  • Conference paper
  • First Online:
Computational Forensics (IWCF 2012, IWCF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 8915))

Abstract

This paper proposes an efficient iris based authentication system. The segmented iris is unwrapped, normalized and enhanced using the proposed local enhancement technique. Occlusion mask determination is performed to detect eyelid, eyelashes and reflections using morphological and filtering operations. Features are extracted and matched from enhanced image using relative intensities of regions and encoding them into a binary template. The proposed recognition approach has obtained a CRR of \(99.07\,\%\) on CASIA-4.0 Interval, \(98.7\,\%\) on CASIA-4.0 Lamp and \(98.66\,\%\) on IITK database. It has also achieved an EER of \(1.82\,\%\) on CASIA-4.0 Interval, \(4.2\,\%\) on CASIA-4.0 Lamp and \(2.12\,\%\) on IITK database.

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 EPUB and 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

References

  1. Adams, R., Bischof, L.: Seeded region growing. IEEE Trans. Pattern Anal. Mach. Intell. 16(6), 641–647 (1994)

    Article  Google Scholar 

  2. Alim, O., Sharkas, M.: Iris recognition using discrete wavelet transform and artificial neural networks. In: IEEE International Symposium on Micro-NanoMechatronics and Human Science. vol. 1, pp. 337–340. IEEE (2003)

    Google Scholar 

  3. Bachoo, A., Tapamo, J.: Texture detection for segmentation of iris images. In: Proceedings of the 2005 annual research conference of the South African institute of computer scientists and information technologists on IT research in developing countries. pp. 236–243. South African Institute for Computer Scientists and Information Technologists (2005)

    Google Scholar 

  4. Badrinath, G.S., Nigam, A., Gupta, P.: An efficient finger-knuckle-print based recognition system fusing SIFT and SURF matching scores. In: Qing, S., Susilo, W., Wang, G., Liu, D. (eds.) ICICS 2011. LNCS, vol. 7043, pp. 374–387. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  5. Bendale, A., Nigam, A., Prakash, S., Gupta, P.: Iris segmentation using improved hough transform. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) ICIC 2012. CCIS, vol. 304, pp. 408–415. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  6. Bendale, A., Nigam, A., Prakash, S., Gupta, P.: Iris segmentation using improved hough transform. In: Huang, D.-S., Gupta, P., Zhang, X., Premaratne, P. (eds.) Emerging Intelligent Computing Technology and Applications. Communications in Computer and Information Science, pp. 408–415. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  7. Cappelli, R., Ferrara, M., Maltoni, D.: Minutia cylinder-code: a new representation and matching technique for fingerprint recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(12), 2128–2141 (2010)

    Article  Google Scholar 

  8. Daugman, J.: Biometric personal identification system based on iris analysis, US Patent (1 March 1994)

    Google Scholar 

  9. Dorairaj, V., Schmid, N., Fahmy, G.: Performance evaluation of iris based recognition system implementing pca and ica encoding techniques. In: Society of Photographic Instrumentation Engineers (SPIE). vol. 5779, pp. 51–58. Citeseer (2005)

    Google Scholar 

  10. He, Z., Tan, T., Sun, Z., Qiu, X.: Robust eyelid, eyelash and shadow localization for iris recognition. In: 15th IEEE International Conference on Image Processing (ICIP), pp. 265–268. IEEE (2008)

    Google Scholar 

  11. Huang, Y., Luo, S., Chen, E.: An efficient iris recognition system. In: Proceedings 2002 IEEE International Conference on Machine Learning and Cybernetics, vol. 1, pp. 450–454. IEEE (2002)

    Google Scholar 

  12. Ma, L., Tan, T., Wang, Y., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(6), 739–750 (2004)

    Article  Google Scholar 

  13. Masek, L., et al.: Recognition of human iris patterns for biometric identification. M. Thesis, The University of Western Australia (2003)

    Google Scholar 

  14. Miyazawa, K., Ito, K., Aoki, T., Kobayashi, K., Nakajima, H.: An efficient iris recognition algorithm using phase-based image matching. In: IEEE International Conference on Image Processing (ICIP), vol. 2, pp. II-49. IEEE (2005)

    Google Scholar 

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

    Article  Google Scholar 

  16. Nigam, A., Anvesh, T., Gupta, P.: Iris classification based on its quality. In: Huang, D.-S., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds.) ICIC 2013. LNCS, vol. 7995, pp. 443–452. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  17. Nigam, A., Gupta, P.: Finger knuckleprint based recognition system using feature tracking. In: Sun, Z., Lai, J., Chen, X., Tan, T. (eds.) CCBR 2011. LNCS, vol. 7098, pp. 125–132. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  18. Nigam, A., Gupta, P.: A new distance measure for face recognition system. In: International Conference on Image and Graphics, ICIG (2009), pp. 696–701 (2009)

    Google Scholar 

  19. Nigam, A., Gupta, P.: Comparing human faces using edge weighted dissimilarity measure. In: International Conference on Control, Automation, Robotics and Vision, ICARCV, pp. 1831–1836 (2010)

    Google Scholar 

  20. Nigam, A., Gupta, P.: Iris recognition using consistent corner optical flow. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012, Part I. LNCS, vol. 7724, pp. 358–369. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  21. Nigam, A., Gupta, P.: Palmprint recognition using geometrical and statistical constraints. In: 2nd International Conference on Soft Computing for Problem Solving, (SocProS), pp. 1303–1315 (2012). http://dx.doi.org/10.1007/978-81-322-1602-5_136

  22. Nigam, A., Gupta, P.: Quality assessment of knuckleprint biometric images. In: International Conference on Image Processing, ICIP. pp. 4205–4209 (2013). http://dx.doi.org/10.1109/ICIP.2013.6738866

  23. Prakash, S., Gupta, P.: An efficient ear recognition technique invariant to illumination and pose. Telecommun. Syst. 52(3), 1435–1448 (2013)

    Article  Google Scholar 

  24. Prakash, S., Jayaraman, U., Gupta, P.: Connected component based technique for automatic ear detection. In: 16th IEEE International Conference on Image Processing (ICIP), 2009, pp. 2741–2744. IEEE (2009)

    Google Scholar 

  25. Singh, N., Nigam, A., Gupta, P., Gupta, P.: Four slap fingerprint segmentation. In: Huang, D.-S., Ma, J., Jo, K.-H., Gromiha, M.M. (eds.) ICIC 2012. LNCS, vol. 7390, pp. 664–671. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  27. Wildes, R.: Iris recognition: an emerging biometric technology. In: Proceedings of the IEEE, 85(9), pp. 1348–1363 (1997)

    Google Scholar 

  28. Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV. pp. 474–485. Academic Press Professional, Inc. (1994)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India

    Aditya Nigam,  Lovish, Amit Bendale & Phalguni Gupta

Authors
  1. Aditya Nigam
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lovish
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amit Bendale
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Phalguni Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lovish .

Editor information

Editors and Affiliations

  1. Indian Statistical Institute, Kolkata, India

    Utpal Garain

  2. School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad, Pakistan

    Faisal Shafait

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Nigam, A., Lovish, Bendale, A., Gupta, P. (2015). Efficient Iris Recognition System Using Relational Measures. In: Garain, U., Shafait, F. (eds) Computational Forensics. IWCF IWCF 2012 2014. Lecture Notes in Computer Science(), vol 8915. Springer, Cham. https://doi.org/10.1007/978-3-319-20125-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20125-2_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20124-5

  • Online ISBN: 978-3-319-20125-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation