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An Investigation of Various Segmentation Methods Used in Iris recognition System

by Tanni Dhoom, Munmun Biswas
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 182 - Number 24
Year of Publication: 2018
Authors: Tanni Dhoom, Munmun Biswas
10.5120/ijca2018918009

Tanni Dhoom, Munmun Biswas . An Investigation of Various Segmentation Methods Used in Iris recognition System. International Journal of Computer Applications. 182, 24 ( Oct 2018), 52-58. DOI=10.5120/ijca2018918009

@article{ 10.5120/ijca2018918009,
author = { Tanni Dhoom, Munmun Biswas },
title = { An Investigation of Various Segmentation Methods Used in Iris recognition System },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2018 },
volume = { 182 },
number = { 24 },
month = { Oct },
year = { 2018 },
issn = { 0975-8887 },
pages = { 52-58 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume182/number24/30086-2018918009/ },
doi = { 10.5120/ijca2018918009 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:12:23.559303+05:30
%A Tanni Dhoom
%A Munmun Biswas
%T An Investigation of Various Segmentation Methods Used in Iris recognition System
%J International Journal of Computer Applications
%@ 0975-8887
%V 182
%N 24
%P 52-58
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A biometric framework offers automatic identification of a human being in view of the special feature or characteristic which is being controlled by the individual. The iris, one of the biometrics emerges among other biometric strategies due to its unique features like stability and accuracy. Iris Recognition has its significant applications in the field of surveillance, forensics and furthermore in security purposes As of late, iris recognition is produced to a few dynamic areas of research, for example, Image Acquisition, restoration, quality assessment, image compression, Image segmentation, noise reduction, normalization, feature extraction, iris code matching, looking vast database, execution under shifting condition and multi bio-metrics. This paper reviews a foundation of iris recognition and literature of late proposed strategies in various fields of iris recognition system.

References
  1. A.V.G.S.Sastry, B. Durga Sri, ”Enhanced Segmentation Method for Iris Recognition”, International Journal of Computer Trends and Technology, volume-4, Issue-2, 2013, Pp 68-71,
  2. Mahmoud Mahlouji , Ali Noruzi , “Human Iris Segmentation for Iris Recognition in Unconstrained Environments”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012, pp 149-155
  3. Nidhi Manchanda, Oves Khan, RishitaRehlan and JyotikaPruthi, “A Survey: Various Segmentation Approaches to Iris Recognition”, International Journal of Information and Computation Technology, Vol. 3, N0. 5 (2013), pp. 419-424
  4. Sruthi.T.KLiterature review: Iris Segmentation Approaches for Iris Recognition Systems International Journal of Computational Engineering Research, Vol. 03, Issue 5, May 2013, pp 67-70
  5. ArezouBanitalebiDehkordi& Syed A.R. Abu-Bakar , “ A REVIEW OF IRIS RECOGNITION SYSTEM”, JurnalTeknologi (Sciences & Engineering), Vol. 77, Issue 1, 2015, pp275–282
  6. YachnaKumari, Mrs.Rohini Sharma, Iris Recognition System using Gabor Filter & Edge Detection, International Journal on Recent and Innovation Trends in Computing and Communication, Volume: 2 Issue: 8, August 2014, pp 2265 – 2269
  7. Neda Ahmadi, GholamrezaAkbarizadeh, “Iris Recognition System based on Canny and LoG Edge Detection Methods”, Journal of Soft Computing and Decision Support Systems, Vol.2 , No.4 , August 2015: pp 26-30
  8. DeepikaPrashar, Mnupreet Kaur , “Human Eye Iris Recognition Using Discrete 2d Reverse Biorthogonal Wavelet 6.8”, INTERNATIONAL JOURNAL OF SCIENTIFIC & TECHNOLOGY RESEARCH VOLUME 3, ISSUE 8, AUGUST 2014, pp 266 -270
  9. Nitasha, Shammi Sharma, Reecha Sharma, ”Comparison Between Circular Hough Transform And Modified Canny Edge Detection Algorithm For Circle Detection”, International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 3, May – 2012, pp 1-5.
  10. R.B. Dubey , Abhimanyu Madan, “ Iris Localization using Daugman’sIntero-Differential Operator”,International Journal of Computer Applications, Volume 93, No 3, May 2014, pp 6-12
  11. Li Ma, Yunhong Wang, Tieniu Tan, “Iris Recognition Based on Multichannel Gabor Filtering”, The 5th Asian Conference on Computer Vision, Melbourne, Australia ,January 2002, pp 23--25.
  12. S.S. Kulkarni, G.H. Pandey, A.S.Pethkar, V.K. Soni, &P.Rathod, “An Efficient Iris Recognition Using Correlation Method”, International Journal of Information Retrieval, ISSN: 0974-6285 Vol. 2, Suppl. Issue 1, 2009, pp. 31-40
  13. Tai Sing Lee, “Image representation using 2D Gabor wavelet “, IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 18, NO. 10, OCTOBER 1996 pp. 959 - 971.
  14. K. C. Chandra sekaran , Dr. K. Kuppusamy , “Efficiency of Gaussian Pyramid Compression Technique for Biometric Images”, IJCSI International Journal of Computer Science Issues, Vol. 11, Issue 3, No 1, May 2014, pp 77-82
  15. MakramNabti,* LahouariGhouti and Ahmed Bouridane, “An effective and fast iris recognition system based on a combined multiscale feature extraction technique”, The journal of the pattern recognition society. vol. 41, 2008, pp 868–879.
  16. Vatsa, M., Singh, R., & Gupta, P. (2004).” Comparison of iris recognition algorithms”. International Conference on Intelligent Sensing and Information Processing, 2004. pp354-358.
  17. Vatsa, Mayank& Singh, Richa&Noore, Afzel. (2008). Improving Iris Recognition Performance Using Segmentation, Quality Enhancement, Match Score Fusion, and Indexing. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetic. Vol. 38, pp. 1021 - 1035.
  18. Mriganawalia, Dr. Shaily Jain, “Iris Recognition System Using Circular Hough Transform”, International Journal of Advance Research in Computer Science and Management Studies, Volume 3, Issue 7, July 2015, pp
  19. PROF. MANISHA MORE, PROF. VISHAK , ”A Survey on Iris Recognition Techniques”, International Journal of Novel Research in Computer Science and Software Engineering Vol. 2, Issue 1, pp: (89-94), Month: January - April 2015,
  20. Chengqiang Liu, MeiXie, “Iris Recognition Based on DLDA”, The 18th International Conference on Pattern Recognition (ICPR'06), 2006, pp 21.
  21. Gafar Zen AlabdeenSalh, Abdelmajid Hassan Mansour, Elnazier Abdallah Mohammed, “Human Iris Recognition Using Linear Discriminant Analysis Algorithm”, International Journal of Computer Applications Technology and Research ,Volume 4– Issue 5, pp. 395 - 404, 2015
  22. Hua Yu, Jie Yang A direct LDA algorithm for high-dimensional data with application to face recognition, The Journal of The Pattern Recognition Society, Published by Elsevier Science Ltd. 2001, pp 2067- 2070
  23. Nivedita S. Sarode, A. M. Patil, ”Review of Iris Recognition: An evolving Biometrics Identification Technology”, International Journal of Innovative Science and Modern Engineering (IJISME),Volume-2 Issue-10, September 2014, pp 34-40
  24. Sonia Sangwan, Reena Rani A Review on: Iris Recognition, International Journal of Advanced Research in Mechanical Engineering & Technology (IJARMET),Vol. 1, Issue 1 (Apr. - Jun. 2015) pp 45-47
  25. Chinni. Jayachandra, H.Venkateswara Reddy, “ Iris Recognition based on Pupil using Canny edge detection and K-Means Algorithm”, International Journal Of Engineering And Computer Science, Volume 2,Issue 1 ,Jan 2013, pp 221-225
  26. D. M. Monro and Z. Zhang, "An effective human iris code with low complexity",  IEEE International Conference on Image Processing 2005, Genova, 2005, pp. III-277.
  27. 2003. Iris Testing of Returning Afghans Passes 200,000 Mark. Available: http://www.unhcr.org/cgi-bin/texis/vtx/search?docid=3f86b4784.
  28. Muron, A., Pospisil, J. (2000). The human iris structure and its usages. Acta Univ. Palacki. Olomuc. Fac. Rerum Nat. Phys, Vol. 39, pp 87–95.
  29. Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol.15 issue11, 1148-1161.
  30. Ma, L., Tan, T., Wang, Y., & Zhang, D. (2003). Personal identification based on iris texture analysis. Pattern Analysis and Machine Intelligence, IEEE Transactions on, Vol. 25 Issue 12, pp 1519-1533.
  31. Wildes, R. P. (1997). Iris recognition: an emerging biometric technology.Proceedings of the IEEE, Vol. 85 Issue 9, pp 1348-1363.
  32. Prof. Teena Varma, Prof. VidyaChitre,Prof.DiptiPatil, “The Haar Wavelet and The Biorthogonal Wavelet Transforms of an Image”, International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 National Conference on Emerging Trends in Engineering & Technology (VNCET-30 Mar’12) pp 288-290
  33. D. de Martin- Roche*, C. Sanchez-Avilat& R. Sanchez-Reillot Iris Recognition for Biometric Identification using DyadicWavelet Transform Zero-Crossing, 272 - 277. 10.1109/.2001.962844.pp 272-277
  34. CASIA V.3 Iris Image Database Version Three. Available: http://www.cbsr.ia.ac.cn.
  35. Daugman, J. 2006. Probing the Uniqueness and Randomness of Iris Codes: Results from 200 Billion Iris Pair Comparisons. Proceedings of the IEEE. 94: 1927-1935.
  36. Quinn, G., Grother, P. and Tabassi, E. 2013. Standard Iris Storage Formats. Handbook of Iris Recognition. Springer. 55-66.
  37. Daugman, J. 2004. How Iris Recognition Works. Circuits and Systems for Video Technology, IEEE Transactions on. Vol. 14: pp 21-30.
  38. Daugman, J. 2007. New Methods in Iris Recognition. Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on. Vol. 37: pp 1167-1175.
  39. http://www.wisegeek.com/what-is-iris-recognitiontechnology.htm
  40. R.C.Gonzalez and R.E, woods, Digital Image Processing, in J.Houseman (2nd Ed.), Handbook of physiology, 4 (New Jersey: Upper Saddle River, 2002).
  41. Z.He, T.Tan, Z.Sun, and X.Qui, ―Towards accurate and fast iris segmentation for iris biometrics,‖ IEEE Trans, On PAMI, Vol. 31, no. 9, pp.1670-1684, Sept. 2009.
  42. A.S Tuama, ―Iris Image Segmentation and Recognition,‖ International Journal of Computer Science & Emerging Technologies, IJCSET, Vol.3, no. 2, , April, 2012.
  43. C.Sanchez-Avilla, R. Sanchez- Reillo, D. de Martin-Roche, ―Iris Based Biometric Recognition using Dyadic Wavelet Transform,‖ IEEE AESS Systems Magazine, Oct. 2002.
  44. F. Yan, X. Shao, G. Li, Z. Sun, and Z. Yang, “Edge detection of tank level IR imaging based on the auto-adaptive double-threshold canny operator,” Intell. Inform. Technology Applicat. Research Assoc., vol. 3, pp. 366 - 370, Dec. 2008.
  45. R. O. Duda and P. E. Hart, “Use of the hough transformation to detect lines and curves in pictures,” Commun. the ACM, vol. 15, pp. 11-15, Jan. 1972.
  46. S. Dey, and D. Samanta, J. Daugman, “How iris recognition works,” IEEE Trans on Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21– 30, 2004.
  47. Iris Recognition [Online] Available: http://en.wikipedia.org/wiki/Iris_recognition
  48. E. Wolff. Antomy of the eye and orbit. 7th edition, H. K. Lewis and Ltd.
  49. I. Daubechies, "Orthonormal Bases of Compactly Supported Wavelets," Comm. Pure Appl. Math., Vol 41, 1988, pp. 906-966.
  50. Haar, Alfréd (1910), "ZurTheorie der orthogonalenFunktionensysteme", MathematischeAnnalen, 69 (3): 331–371
  51. Lee, B.; Tarng, Y. S. (1999). "Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current". International Journal of Advanced Manufacturing Technology. 15 (4): 238–243.
  52. Daugman, J. 2003. The Importance of Being Random: Statistical Principles of Iris Recognition. Pattern Recognition. 36: 279-291.
  53. Monro, D. M. and Zhang, D. 2005. An Effective Human Iris Code with Low Complexity. IEEE International Conference on Image Processing. 3: 277-80.
  54. http://sepwww.stanford.edu/data/media/public/sep/morgan/texturematch/paper_html/node3.html
  55. Li, S. Z. (1998). "Face recognition based on nearest linear combinations". In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pages 839-844, Santa Barbara, CA.
  56. http://www.scholarpedia.org/article/Nearest_feature_line
  57. Im proceedings Proenca. Hugo and Alexandre, Luis A., ―UBIRIS: A noisy iris image database,‖ Proceedings of ICIAP 2005- International Conference on Image Analysis and Processing, vol. 1, pp. 970-977.
Index Terms

Computer Science
Information Sciences

Keywords

Biometric Iris Segmentation Feature extraction

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