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Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Adebayo Omotosho, Omotanwa Adegbola, Michael Edobor
10.5120/ijca2016912460

Adebayo Omotosho, Omotanwa Adegbola and Michael Edobor. Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises. International Journal of Computer Applications 155(10):44-52, December 2016. BibTeX

@article{10.5120/ijca2016912460,
	author = {Adebayo Omotosho and Omotanwa Adegbola and Michael Edobor},
	title = {Reanalyzing Li and Tao. (2014): Investigating Algorithm Recognition on Dark Irises},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {155},
	number = {10},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {44-52},
	numpages = {9},
	url = {http://www.ijcaonline.org/archives/volume155/number10/26645-2016912460},
	doi = {10.5120/ijca2016912460},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Iris recognition algorithms have been proposed in several works with some of these algorithms solving mainly templates identification accuracy issues. The need to test these algorithms for identification or matching speed cannot be over-emphasized as this is also important when deploying algorithms in real application. This aim of this paper is to implement and validate a selected iris recognition algorithm. Performance evaluation was performed with the sole purpose of verifying the literature reported accuracy for the selected algorithm as well as to compute its identification speed on two databases (CASIA and BuIris) containing 600 iris images each. Results obtained matched the earlier 0% false acceptance with CASIA database but 42.3% with BuIris. This paper results verifies the scope of this algorithm and the need for improvement that could increase its adoptability globally.

References

  1. Wang L, Geng X. Behavioral biometrics for human identification: Intelligent applications. Hershey, PA: IGI Global; 2010.
  2. Bakshi S, Mehrotra H, Majhi B. Postmatch pruning of SIFT pairs for iris recognition. International Journal of Biometrics. 2013; 5(2):160-80.
  3. Kindt E. Privacy and data protection issues of biometric applications. Law, Governance and Technology Series. 2013; 12.
  4. Wildes RP. Iris recognition: an emerging biometric technology. Proceedings of the IEEE. 1997; 85(9):1348-63.
  5. Fakhry HH, Cardozo BB. Research and Development of an Iris-Based Recognition System for Identification and Secure Authentication. INFORMATION AND SECURITY. 2006; 19:39.
  6. Ross A. Iris recognition: The path forward. Computer. 2010; 43(2):30-5.
  7. Yang K, Du EY. Speed-up multi-stage non-cooperative iris recognition. International Journal of Biometrics. 2012; 4(4):406-21.
  8. Beleza S, Johnson NA, Candille SI, Absher DM, Coram MA, Lopes J, Campos J, Araújo II, Anderson TM, Vilhjálmsson BJ, Nordborg M. Genetic architecture of skin and eye color in an African-European admixed population. PLoS Genet. 2013; 9(3):e1003372.
  9. Badejo JA, Majekodunmi TO, Atayero AA. Development of CUiris: A Dark-Skinned African Iris Dataset for Enhancement of Image Analysis and Robust Personal Recognition. World Congress on Engineering and Computer Science, San Francisco, USA. 2011; 624-629
  10. Tisse CL, Martin L, Torres L, Robert M. Person identification technique using human iris recognition. In Proc. Vision Interface 2002 May 27; 2002. p. 294-299.
  11. Daugman J. Recognising persons by their iris patterns. In Advances in Biometric Person Authentication. Springer Berlin Heidelberg. 2004. p. 5-25.
  12. Arivazhagan S, Ganesan L, Srividya T. Iris recognition using multi-resolution transforms. International Journal of Biometrics. 2009; 1(3):254-67.
  13. Lu Y, He X, Wen Y, Wang PS. A new cow identification system based on iris analysis and recognition. International Journal of Biometrics. 2014; 6(1):18-32.
  14. Pankanti S, Bolle RM, Jain A. Biometrics: The future of identification [Guest Eeditors' Introduction]. Computer. 2000; 33(2):46-9.
  15. Liu S, Silverman M. A practical guide to biometric security technology. IT Professional. 2001; 3(1):27-32.
  16. Bowyer KW, Hollingsworth KP, Flynn PJ. A survey of iris biometrics research: 2008–2010. In Handbook of iris recognition. Springer London; 2013. p. 15-54.
  17. Granger E, Khreich W, Sabourin R, Gorodnichy DO. Fusion of biometric systems using Boolean combination: an application to iris-based authentication. International Journal of Biometrics. 2012; 4(3):291-315.
  18. Dey S, Samanta D. Fast and accurate personal identification based on iris biometric. International Journal of Biometrics. 2010; 2(3):250-81.
  19. Latha L, Thangasamy S. Efficient method of person authentication based on fusion of best bits in left and right irises. International Journal of Biometrics. 2012; 4(3):203-19.
  20. Sulochana CH, Selvan S. Robust iris recognition algorithm for non-cooperative environment. International Journal of Biometrics. 2009; 2(1):71-86.
  21. El-Abed M, Giot R, Hemery B, Rosenberger C. Evaluation of biometric systems: A study of users’ acceptance and satisfaction. International Journal of Biometrics. 2012; 4(3):265-90.
  22. Hanna KJ, Mandelbaum R, Mishra D, Paragano V, Wixson LE. A System for Non-Intrusive Human Iris Acquisition and Identification. In MVA 1996 Nov 12; 1996. p. 200-203.
  23. Boles WW, Boashash B. A human identification technique using images of the iris and wavelet transform. IEEE transactions on signal processing. 1998; 46(4):1185-8.
  24. Lim S, Lee K, Byeon O, Kim T. Efficient iris recognition through improvement of feature vector and classifier. ETRI journal. 2001; 23(2):61-70.
  25. Ma L, Wang Y, Tan T. Iris recognition based on multichannel Gabor filtering. InProc. Fifth Asian Conf. Computer Vision 2002 Jan 23; 2002; 1: 279-283.
  26. Miyazawa K, Ito K, Aoki T, Kobayashi K, Nakajima H. A phase-based iris recognition algorithm. In International Conference on Biometrics. Springer Berlin Heidelberg; 2006. p. 356-365
  27. Sanchez-Avila C, Sanchez-Reillo R. Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation. Pattern Recognition. 2005; 38(2):231-40.
  28. Monro DM, Rakshit S, Zhang D. DCT-based iris recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2007; 29(4):586-95.
  29. Vatsa M, Singh R, Noore A. Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2008; 38(4):1021-35.
  30. Vatsa M, Singh R, Noore A. Reducing the False Rejection Rate of Iris Recognition Using Textural and Topological. Int. J. Signal Proc. 2005; 2(2).
  31. Ghassan J, Mohammed HB, Ann A. A new localization algorithm for iris recognition. Information Technology Journal. 2009; 8(2):226-30.
  32. Kohli N, Yadav D, Vatsa M, Singh R. Revisiting iris recognition with color cosmetic contact lenses. In2013 International Conference on Biometrics (ICB) 2013 Jun 4; IEEE. 2013. p. 1-7.
  33. Sarhan AM. Iris Recognition Using Discrete Cosine Transform and. Journal of Computer Science. 2009; 5(5):369-73.
  34. De Villar JA, Ives RW, Matey JR. Design and implementation of a long range iris recognition system. In 2010 Conference Record of the Forty Fourth Asilomar Conference on Signals, Systems and Computers. IEEE. 2010. p. 1770-1773.
  35. Kadry S, Smaili M. Wireless attendance management system based on iris recognition. Scientific Research and Essays. 2013; 5(12):1428-35.
  36. Abdullah M, Al-Dulaimi F, Al-Nuaimy W, Al-Ataby A. Efficient small template iris recognition system using wavelet transform. International Journal of Biometric and Bioinformatics. 2011; 5(1):16-27.
  37. Cho ES, Caytiles RD, Kim SS. New Algorithm Biometric-Based Iris Pattern Recognition System: Basis of Identity Authentication and Verification. Journal of Security Engineering. 2011; 8(5).
  38. Chaudhary U, Mubarak CM. Iris Recognition Using BPNN Algorithm’, International Journal of Engineering Research and Applications (IJERA), National Conference on Emerging Trends in Engineering & Technology, 2012; pp.203 – 208.
  39. Memane MM, Ganorkar SR. RED Algorithm based Iris Recognition. genetics. 2012; 1:2.
  40. Verma P, Dubey M, Verma P, Basu S. Daughman’s algorithm method for iris recognition—a biometric approach. International Journal of Emerging Technology and Advanced Engineering. 2012; 2(6):177-85.
  41. Han WY, Chen WK, Lee YP, Wu KS, Lee JC. Iris recognition based on local mean decomposition. Appl. Math. 2014; 8(1L):217-22.
  42. Khan YD, Khan SA, Ahmad F, Islam S. Iris recognition using image moments and k-means algorithm. The Scientific World Journal. 2014; 2014: 1-9.
  43. Li Y, Tao C. (2014). Iris Recognition Based on Improved Compressive Sensing Algorithm. Journal of Information & Computational Science. [cited 2016 September 4]; 5349–5355. Available from: http://www.joics.com;
  44. Rakate NT, Patil UA. An Efficient Iris Recognition System using Dual-tree Complex Wavelet Transform. International Journal of Computer Science and Information Technologies. 2014; 5(3): 2749-2754
  45. Sun J, Lu ZM, Zhou L. Iris Recognition using curvelet transform based on principal component analysis and linear discriminant analysis. J Inf Hiding Multimedia Signal Process. 2014; 5(3):567-73.

Keywords

Iris recognition; biometrics; empirical analysis; Casia; BuIris