Journal Title : International Journal of Modern Trends in Engineering and Science

Author’s Name : Nagarajan P, Ramesh S M, Sundrarajan T V P

Volume 01 Issue 10  Year 2014  

ISSN no:  2348-3121 

Page no: 11-14

Abstract— Iris  recognition  is  the  most  accurate  biometric identification system on hand. Most iris recognition systems use algorithms developed by Daugman. The performance of iris recognition is highly depends on edge detection. Canny is the edge detector which is commonly used.  The  objectives  of  this research  are  to  a)  study  the  edge  detection  criteria  and  b) measure the PSNR values in estimating the noise between the original iris feature and new iris template. The eye image with 320×280 dimensions is obtained from the database which has been preprocessed through the segmentation and normalization in obtaining the rubber sheet model with 20×240 in dimension.

Keywords— Edge detection, Feature extraction, Iris recognition system, PSNR.


[1]  J. Daugman and C. Downing, “Epigenetic randomness, complexity and singularity of human iris patterns., ” Proceedings. Biological sciences / The Royal Society, Vol. 268, No. 1477, pp. 1737-40, Aug.(2001).
[2]  J. G. Daugman, “Demodulation by complex-valued wavelets for stochastic pattern recognition, ” International Journal of Wavelets, Multiresolution and Information Processing, Vol. I, No. I, pp. 1-17,(2003).
[3]  R. P. Wildes, J. C. Asmuth, G. L. Green, S. C. Hsu, R.J. Kolczynski, J. R. Matey, and S. E. McBride, “A system for automated iris recognition, ” Proceedingsof 1994 IEEE Workshop on Applications of Computer Vision, pp. 121-128, (1994).
[4] L. Masek, “Recognition of Human Iris Patterns for Biometric Identification, ” The University of Western Australia,(2003).
[5] M. M. Khaladkar and S. R. Ganorkar, “A Novel Approach for Iris Recognition, ” Vol. I, No. 4,(2012).
[6] M. Z. Rashad, M. Y. Shams, and o. Nomir, “iris recognition based on lbp and, ” Vol. 3, No. 5,(2011).
[7] I. No and S. Chawla, “Available Online at www.ijarcs.info A Robust Segmentation Method for Iris Recognition, ” Vol. 2, No. 5, pp. 340-343,(2011).
[8] B. C. Kovoor, M. H. Supriya, and K. P. Jacob, “iris biometric recognition system employing canny operator, ” pp. 65-74,(2013).
[9]  V. R. E. C, “Iris Texture Analysis for Security Systems, ” Vol. 64, No. 22, pp. 37-44,(2013).
[10] H. R. Gite and C. N. Mahender, “iris code generation and recognition, ” Vol. 3, No. 3, pp. 103-107,(2011).
[11] K. W. Bowyer, K. P. Hollingsworth, and P. 1.Flynn, Handbook of Iris Recognition. London: Springer London, (2013), pp.(2008-2010).
[12] S. Yang, M. Wang, Y. Sun, F. Sun, and L. Jiao, “Compressive Sampling based Single-Image Superresolution Reconstruction by dual-sparsity and Non- local Similarity Regularizer, ” Pattern Recognition Letters, Vol. 33, No. 9, pp. 1049-1059, Jul.(2012).
[13] L. Machala, P. Tichavsky, and J. POSP!, “Human eye iris recognition using the mutual information, ” Vol. 9, No. 9, pp. 399-404,(2004).
[14] H. Patel, C. K. Modi, M. C. Paunwala, and S. Patnaik,”Human Identification by Partial Iris Segmentation Using Pupil Circle Growing Based on Binary Integrated Edge Intensity Curve, ” (2011) International Conference on Communication Systems and Network Technologies, pp. 333-338, Jun.(2011).

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