Skip to main content
Log in

Iris data encryption based on Aztec Symbology

  • Original Paper
  • Published:
Evolving Systems Aims and scope Submit manuscript

Abstract

Iris recognition is one of the important authentication mechanisms; authentication needs verification of individuals for uniqueness hence converting iris data into barcode is an appropriate in authenticating individuals to identify uniqueness. Such converted barcode is unique for every iris image. In iris recognition, most applications capture the eye image; extract the iris features and stores into the database in digitized form. The size of the digitized form is equal to or little less than original iris image. This as leads to the drawbacks such as more usage of memory and more time required for searching and matching operations. To overcome these drawbacks we propose an approach wherein we convert extracted iris features into barcodes. This transformation of iris into barcode reduces the space for storage and the time required for searching and matching operations, which are essential features in real time applications.

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

  • Al-Raisi AN, Al-Khouri AM (2008) Iris recognition and the challenge of homeland and border control security in UAE. Telematics Inform 25(2):117–132

    Article  Google Scholar 

  • Andrew L, Robb H (1995) Two dimensional data encoding structure and symbology for use with optical fibres, United States Patent, Patent Number: 5591956, May 15

  • Canny J (1986) A computational approach to edge detection. In: Pattern analysis and machine intelligence, IEEE transactions, vol 6, pp 679–698

  • Chanda B, Mujumder DD (2011) Digital image processing and analysis EEE, 2nd edn, PHI Learning Private Limited, Delhi

  • Czajka A, Strzelczyk P, Chochowski M, Pacut A (2007) Iris recognition with match-on-card. In: 15th European Signal Processing Conference, September 2007, Pozman, pp 189–192

  • Jain AK, Ross A, Prabhakar S (2004) An introduction to biometric recognition. IEEE Trans Circuits Syst Video Technol 14(1):4–20

    Article  Google Scholar 

  • Kong WK, Zhang D (2001) Accurate iris segmentation based on novel reflection and eyelash detection model. In: Proceedings of IEEE international symposium on Intelligent multimedia, video and speech processing 2001, pp 263–266

  • Kulkarni SB, Hegadi RS, Kulkarni UP (2011). Improvement to libor Masek algorithm of template matching method for iris recognition. In: Proceedings of the International Conference and Workshop on Emerging Trends in Technology, ACM pp 1270–1274

  • Masek L, Kovesi P (2003). Matlab source code for a biometric identification system based on iris patterns, vol 2(4) The School of Computer Science and Software Engineering, The University of Western Australia

  • Miyazawa K, Ito K, Aoki T, Kobayashi K, Nakajima H (2006) A phase-based iris recognition algorithm. In: Advances in biometrics: international conference, ICB 2006, Hong Kong, China, January 5–7, proceedings. Springer, New York Inc, p 356

  • Rathgeb C, Uhl A (2010) Secure iris recognition based on local intensity variations. In: Proceedings of the 7th international conference on image analysis and recognition, vol (Part II). Springer, Berlin, pp 266–275

  • 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

    Google Scholar 

  • Schuckers SA, Schmid NA, Abhyankar A, Dorairaj V, Boyce CK, Hornak LA (2007) On techniques for angle compensation in nonideal iris recognition. IEEE Trans Syst Man Cybern Part B Cybern 37(5):1176–1190

    Article  Google Scholar 

  • Sylvester (2001) J Reed Solomon Codes

  • Tang RN, Han JQ, Zhang XM (2009) Efficient iris segmentation method with support vector domain description. Optica Appl 39(2):365

    Google Scholar 

  • Vatsa M, Singh R, Noore A (2008) Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans Syst Man Cybern Part B Cybern 38(4):1021–1035

    Article  Google Scholar 

  • Voke J (1999) Radiation effects on the eye. Part 3b. Ocular effects of ultraviolet radiation. Optom Today 1999:37–40

    Google Scholar 

  • Wildes R, Asmuth J Green G Hsu S, Kolczynski R, Matey J, McBride S (1994) A system for automated iris recognition. In: Proceedings IEEE workshop on applications of computer vision, Sarasota, pp 121–128

Download references

Acknowledgments

The authors wish to thank CASIA and AICTE. “Portions of the research in this paper use the CASIA-IrisV3 collected by the Chinese Academy of Sciences’ Institute of Automation (CASIA)” and a reference to “CASIA-IrisV3, http://www.cbsr.ia.ac.cn/IrisDatabase.htm”. The work is partially supported by the Research Grant from AICTE, Govt. of India, Reference No: 8023/RID/RPS-114(PVT)/2011-12 Dated December, 24-2011.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shrinivasrao B. Kulkarni.

Appendix 1

Appendix 1

Aztec Symbols mapping value table

  1. Source: Andrew Longacre and Robb Hussey (1995)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kulkarni, S.B., Hegadi, R.S. & Kulkarni, U.P. Iris data encryption based on Aztec Symbology. Evolving Systems 4, 203–217 (2013). https://doi.org/10.1007/s12530-013-9075-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12530-013-9075-8

Keywords

Navigation