CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Iris Indexing Techniques: A Review

by N. Poonguzhali, M. Ezhilarasan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 73 - Number 18
Year of Publication: 2013
Authors: N. Poonguzhali, M. Ezhilarasan
10.5120/12842-0171

N. Poonguzhali, M. Ezhilarasan . Iris Indexing Techniques: A Review. International Journal of Computer Applications. 73, 18 ( July 2013), 23-29. DOI=10.5120/12842-0171

@article{ 10.5120/12842-0171,
author = { N. Poonguzhali, M. Ezhilarasan },
title = { Iris Indexing Techniques: A Review },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 73 },
number = { 18 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 23-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume73/number18/12842-0171/ },
doi = { 10.5120/12842-0171 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:40:28.178497+05:30
%A N. Poonguzhali
%A M. Ezhilarasan
%T Iris Indexing Techniques: A Review
%J International Journal of Computer Applications
%@ 0975-8887
%V 73
%N 18
%P 23-29
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The objective of this paper is to present the state of art in iris indexing. The potential raise of accurateness along with enhanced robustness beside forgeries makes in fact iris recognition a promising field for research. The performance of a biometric system is evaluated based on the retrieval time and error rate which are dependent on the size of the database and hence the need for indexing. Iris indexing can be categorized based on the texture analysis, color and SFIT key point. Further the paper discusses the description of some databases used for indexing techniques to prove the efficiency. The performance evaluation metrics are also discussed.

References
  1. Jain, A. Ross and S. Prabhakar, "An Introduction to Biometric Recognition," IEEE Transactions on Circuits and Systems on Video Technology, vol. 14, no. 1, pp. 4–20, Jan 2004.
  2. K. W. Bowyer, K. Hollingsworth, and P. J. Flynn, "Image understanding for iris biometrics: A survey," Computer. Vis. Image Understand. , vol. 110, no. 2, pp. 281–307, May 2008.
  3. U. I. D. of India. http://www. uidai. gov. in/, Last accessed on Feb 02, 2012.
  4. L. Ma, T. Tan, Y. Wang, and D. Zhang, "Efficient iris recognition by characterizing key local variations," IEEE Trans. Image Process. , vol. 13, no. 6, pp. 739–750, Jun. 2004.
  5. R. Mukherjee and A. Ross, "Indexing iris images," in Proc. 19th Int. Conf. Pattern Recognit. , 2008, pp. 1–4.
  6. Yulin Si, Jiangyuan Mei, and Huijun Gao, "Novel Approaches to Improve Robustness, Accuracy and Rapidity of Iris Recognition Systems", IEEE Transactions on Industrial Informatics, vol. 8, no. 1, pp. 110-117, February 2012.
  7. P. Hsiao, C. Lu, and L. Fu, "Multilayered image processing for multiscale Harris corner detection in digital realization," IEEE Trans. Ind. Electron. , vol. 57, no. 5, pp. 1799–1805, 2010.
  8. H. Mehrotra, B. Majhi, and P. Gupta, "Robust iris indexing scheme using geometric hashing of SIFT keypoints," J. Network Comput. Appl. , vol. 33, no. 3, pp. 300–313, 2010.
  9. Hunny Mehrotra, Badrinath G. Srinivas, Banshidhar Majhi,and Phalguni Gupta, "Indexing Iris Biometric Database Using Energy Histogram of DCT Subbands", Journal of Communication in computer and Information Science, vol. 40, pp. 194-204, 2009.
  10. D. J. Kerbyson, T. J. Atherton, "Circle detection using Hough transform filters" Fifth International Conference on Image Processing and its Applications, pp. 370–374, 1995.
  11. John Daugman, "How Iris Recognition Works", IEEE Transactions on Circuits And Systems For Video Technology, Vol. 14, No. 1, January 2004.
  12. S. A. Khayam, "The Discrete Cosine Transform (DCT): Theory and Application", Tutorial Report, Michigan State University, 2003.
  13. U. Jayaraman, S. Prakash, and P. Gupta, "An efficient technique for indexing multimodal biometric databases", International. Journal of Biometrics, vol. 1, no. 4, pp. 418–441, 2009.
  14. L. Ma, T. N. Tan, Y. H. Wang, and D. Zhang, "Local intensity variation analysis for iris Recognition", Pattern Recognition, Vol. 37, No. 6, pp. 1287–1298, 2004.
  15. R. C. Gonzalez, and R. E. Woods, "Digital image processing", Addison-Wesley Longman Publishing Co. , Inc. 2001.
  16. Mayank Vatsa, Richa Singh, Afzel Noore, "Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing" IEEE Transactions on Systems, Man, and Cybernetics, Part B vol. 38, no. 4, pp. 1021-1035, 2008
  17. Tsai, A. Yezzi, Jr. , and A. Willsky, "Curve evolution implementation of the Mumford–Shah functional for image segmentation, denoising, interpolation, and magnification," IEEE Trans. Image Process. , vol. 10, no. 8, pp. 1169–1186, Aug. 2001.
  18. R. Singh, M. Vatsa, and A. Noore, "Improving verification accuracy by synthesis of locally enhanced biometric images and deformable model," Signal Process. , vol. 87, no. 11, pp. 2746–2764, Nov. 2007.
  19. M. Vatsa, R. Singh, and A. Noore, "Reducing the false rejection rate of iris recognition using textural and topological features," Int. J. SignalProcess. , vol. 2, no. 1, pp. 66–72, 2005.
  20. Ravindra Gadde, D. Adjeroh, A. Ross, "Indexing Iris Images Using Burrow Wheelers Transform", Proc of IEEE International Workshop on Information Forensics and Security, Dec 2010.
  21. C. Rathgeb, A. Uhl, "Iris-Biometric Hash Generation for Biometric Database Indexing", 20th International. Conference on Pattern Recognition, Dec 2010.
  22. N. B. Puhan, N. Sudha, "A novel iris database indexing method using the iris color," Proc. 3rd IEEE Conf. on Industrial Electronics and Applications (ICIEA), pp. 1886-1891, 2008.
  23. Umarani J, Surya Prakash and Phalguni Gupta, "An Iris Retrieval Technique Based on Color and Texture", Indian Conference on Computer Vision, Graphics and Image Processing, pp. 93-100, Dec 2010.
  24. Qin Zhao, "A new approach for noisy iris database indexing based on color information", The 6th International Conference on Computer Science & Education, pp 28-31, August 2011.
  25. Hunny Mehrotra, Banshidhar Majhi, Phalguni Gupta, "Robust iris indexing scheme using geometric hashing of SIFT keypoints", Journal of Network and Computer Applications vol. 33 no. 3, pp. 300-313, 2010.
  26. D. G. Lowe, "Distinctive image features from scale-invariant keypoints", International Journal on Computer Vision, vol. 60, no. 2, pp. 91–110, 2004.
  27. Rigoutsos, R. Hummel, "Implementation of geometric hashing on the Connection Machine" Workshop on directions in automated CAD-based vision, pp. 76–84, 1991.
  28. Michal Dobeš and Libor Machala, Iris Database, http://www. inf. upol. cz/iris/
  29. [Online]. http://iris. nist. gov/ice/ICE Home. htm
  30. H. Proenca and Alexandre, "UBIRIS: A Noisy Iris Image Database", Lecture Notes in Computer Science, vol. 1, pp. 970-977, 2005.
  31. Online. Available:http://www. cbsr. ia. ac. cn/IrisDatabase/irisdatabase. php
Index Terms

Computer Science
Information Sciences

Keywords

Biometric iris indexing performance metrics databases

Powered by PhDFocusTM