This paper proposes an iris recognition algorithm based on iris images. It consists of five major steps i.e., iris acquisition, localization, normalization, feature extraction and matching. The inner pupil boundary is localized using Circular Hough Transformation. The technique performs better in the case of occlusions and images muddled by artifacts such as shadows and noise. The outer iris boundary is detected by circular summation of intensity approach from the determined pupil center and radius. The localized iris image is transformed from Cartesian to polar co-ordinate system to handle different size, variation in illumination and pupil dilation. Corners in the transformed iris image are detected using covariance matrix of change in intensity along rows and columns. All detected corners are considered as features of the iris image. For recognition through iris, corners of both the iris images are detected and total number of codes that are matched between the two images are obtained. The two iris images belong to the same person if the number of matched corners is greater than some threshold value.

1. L. Flom and A. Safir: Iris Recognition System.U.S. Patent No.4641394 (1987)
2. J. G. Daugman: High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence Vol. 15 (1993) 1148–1161
3. W.W. Boles, B. Boashah: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transaction on Signal Processing Vol. 46 (1998) 1185-1188
4. R. Wildes, J. Asmuth, G. Green, S. Hsu, R.Kolczynski, J. Matey, S. McBride: A Machine-vision System for Iris Recognition. Machine Vision and Applications Vol. 9 (1996) 1-8
5. Leila Fallah Araghi, Hamed Shahhosseini, Farbod Setoudeh.Iris Recognition using neural network.proceedings of the International Multiconference of engineers and computer scientist,2010 vol I, IMECS 2010.
6. T. Chuan Chen, K. Liang Chung: An Efficient Randomized Algorithm for Detecting Circles.Computer Vision &Image Understanding Vol. 83 (2001) 172-191
7. E. R. Davies: Machine Vision. 3rd Edition: Elsevier (2005)
8. J. Canny: A Computational Approach to Edge Detection. IEEE Transaction on Pattern Analysis and Machine Intelligence Vol. 8 (1986) 679-714
9. R. C. Gonzalez, R. E. Woods: Digital Image Processing. 2nd Edition, Pearson Education, India (2002)
10. . Y. Zhu, T. Tan, Y. Wang: Biometric Personal Identification Based on Iris Patterns. Proced dings of ICPR, International Conference on Pattern Recognition Vol. II (2000) 805-808
11. C. R. Prashanth1, Shashi kumar D.R.2, K. B. Raja, K.R.Venugopal, L. M. Patnaik High security: Human recognition system using Iris Images. International Journal of Recent Trends in Engineering, Vol. 1, No. 1, May 2009
12. Lionel MARTIN: Person identification technique using human iris recognition Advanced system technologies Microelectron- ZI rousset-13106 rousset Cedex, France 2010.

Biometrics Circular Hough transform Hamming Distance