Iris Recognition and its Protection Overtone using Cryptographic Hash Function
|International Journal of Computer Science and Engineering|
|© 2016 by SSRG - IJCSE Journal|
|Volume 3 Issue 5|
|Year of Publication : 2016|
|Authors : Gatheejathul Kubra.J, Rajesh.P|
How to Cite?
Gatheejathul Kubra.J, Rajesh.P, "Iris Recognition and its Protection Overtone using Cryptographic Hash Function," SSRG International Journal of Computer Science and Engineering , vol. 3, no. 5, pp. 1-9, 2016. Crossref, https://doi.org/10.14445/23488387/IJCSE-V3I5P101
To overcome the problems faced in security there are many advanced techniques used nowadays. People individually use their finger prints, voice, face reactions, eyes as a password for security purposes .Iris is the part of eye which is unique for every human being. Iris recognition is the secured technique being used for adhar card. Here we use cryptographic hashing function that is used to verify data integrity through the creation of a 128-bit message digest from data input. The objective of ‘IRIS Recognition’ is primarily to improve the security of biometric recognition technology that uses IRIS. Though it enjoys clear advantage over other methods using finger prints, voice recognition, face recognition etc. in current technology. After the conversion of iris patterns into linear graph, the threshold value is taken which makes it prone to hacking. It is this vulnerability that the proposed technology will address by assigning a random number to the mean value. Thus the proposed technology will be a step forward in enhancing the security of iris based biometric systems.
Iris Recognition, Edge Detection, Pupil detection, Normalization, Feature extraction.
 P .Vijayaraagavan, R.C Narayanan “Web Image Re-Ranking Using Hash Based Signature”SSRG-IJCSE-V213P115.
 P.Kalavathi,”A Thresholding Method for Color Image Binarization” SSRG-IJCSE-V117P107
 R. Connaughton, A. Sgroi, K. Bowyer, and P. Flynn, “A multi algorithm analysis of three Iris biometric sensors,” IEEE Trans. Inf. Forensics Security, vol. 7, no. 3, pp. 919– 931, Jan. 2012.
 J. J. Nichols. (2012). Annual Report: Contact Lenses [Online]. Available: http://www.clspectrum.com/articleviewer.aspx?articleID=10 7853
 N. Kohli, D. Yadav, M. Vatsa, and R. Singh, “Revisiting Iris recognition with color cosmetic contact lenses,” in Proc. 6th IAPR, 2013, pp. 1–5.
 M. Negin, T. Chmielewski, M. Salganicoff, U. von Seelen, P. Venetainer, and G. Zhang, “An Iris biometric system for public and personal use,” IEEE Compute., vol. 33, no. 2, pp. 70–75, Feb. 2000.
 J. W. Thompson, H. Santos-Villalobos, P. J. Flynn, and K. W. Bowyer, “Effects of Iris surface curvature on Iris recognition,” in Proc. 6th IEEE Int. Conf. Biometrics, Technol., Appl., Syst., 2013, pp. 1–3.
 S. Baker, A. Hentz, K. Bowyer, and P. Flynn, “Degradation of Iris recognition performance due to non-cosmetic prescription contact lenses,” Compute. Vis. Image Understand., vol. 114, no. 9, pp. 1030–1044, 2010.
 J. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition,” Int. J. Wavelets,
Multiresolution Inf. Process., vol. 1, no. 1, pp. 1–17, 2003.  E. C. Lee, K. R. Park, and J. Kim, “Fake Iris detection by using purkinje image,” in Proc. IAPR Int. Conf. Biometrics, 2006, pp. 397–403.
 X. He, S. An, and P. Shi, “Statistical texture analysis-based approach for fake Iris detection using support vector machines,” in Proc. IAPR Int. Conf. Biometrics, 2007, pp. 540–546.
 Z. Wei, X. Qiu, Z. Sun, and T. Tan, “Counterfeit Iris detection based on texture analysis,” in Proc. 18th Int. Conf. Pattern Recognit., 2008, pp. 1–4.
 Z. He, Z. Sun, T. Tan, and Z. Wei, “Efficient Iris spoof detection via boosted local binary patterns,” in Advances in Biometrics. New York, NY, USA: Springer-Verlag, 2009, pp. 1080–1090.