A Fast and Accurate Circular Segmentation Method for Iris Recognition Systems

Walid Aydi(1*), Nouri Masmoudi(2), Lofti Kamoun(3)

(1) National Engineering School of Sfax, Tunisia, Tunisia
(2) SFAX Laboratory of Electronics and Information Technology, Tunisia
(3) LETI: Laboratoire d'Electronique et des Technologies de l'Information, Tunisia
(*) Corresponding author


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Abstract


With the development of the identification methods, an iris recognition system is expected to be one of the basic elements of modern society, with many application areas such as access control, national ID cards, etc. Any iris recognition system follows four functioning steps: Segmentation, Normalization, Encoding, and Matching. Iris segmentation is a key step in any iris recognition system and it directly affects the accuracy of matching. Several methods have been suggested to assess iris regions with two non-concentric circles. The circle model was investigated to find a tradeoff between modeling complexity, accuracy of the algorithm and computational time. In this paper, we propose an enhancement in the form of a modified Masek approach and a comparative study of the performance of three methods: radial segmentation, Masek approach and ours which are all parts of circle model approaches. Using CASIA Iris Database V3.0, our experimental results reveal that the proposed method provides a high performance in time and accuracy
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Keywords


Biometrics; Iris Segmentation; Pupil; Circle Model; Eyelids

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References


N. OTSU, A threshold selection method from gray level histogram, IEEE Transactions on systrems, man, and cybernetics, vol. 9, n.1, pp. 62 – 66, 1979.

http://www.cbsr.ia.ac.cn/IrisDatabase.htm.

R. G. KEYS, Cubic convolution interpolation for digital image processing, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 29, n. 6, 1981.

J. Daugman, How iris recognition works, IEEE Transactions on circuits and systems for video technology, vol. 14, no. 1, 2004.

L. Masek, Recognition of human iris patterns for biometric identification, Ph.D. Thesis, The University of Western Australia, 2003.

R. Wildes, Iris Recognition: An Emerging Biometric Technology, Proccedings of the IEEE, vol. 85, no. 9, pp. 1348–1365, 1997.

D. Jacobs, Image gradients, (class notes for CMSC 426, 2005).

E Sung, X. Chen, J. Zhu, J. Yang, Towards non cooperative biometric iris recognition systems, 7th International Conference on Control, Automation, Robotics and Vision, vol.2, pp 990 - 995, 2002.

R. Mukherjee and A. Ross , Indexing Iris Images, 19th International Conference on Pattern Recognition (ICPR), pp 1 - 4, 2008.

J. BURGE Mark, W. BOWYER Kevin, Handbook of iris recognition, (Springer, 2013).

B. Akrout, I. khanfir kallel, C. ben amar and B. amor, A new scheme of signature extarction for iris Authentication, 6th international Multi-conference on systems signals and devices, 2009.

M. Villamizar1, A. Sanfeliu1 and J. Andrade-Cetto , Computation of Rotation Local Invariant Features using the Integral Image for Real Time Object Detection, 18th International Conference on Pattern Recognition, pp 81 – 85, 2006.

M. Breidt, Be gamma correct, 2009.

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, n.11, pp 1148 – 1161, 1993.

J. Daugman, Biometric personal identification system based on iris analysis, United States Patent Number: 5,291,560, 1994.

I. K. Kallel ,D. S. Masmoudi, N.Derbel, Fast pupil location for better iris detection, 6th international Multi-conference on Systems, Signals and devices, pp. 1-6, 2009.

A.B. Grugory, digital image processing: principles and applications, (Wiley, 1994).

I. Khanfir, A. Kallel, K. Taouil, M. Salim Bouhlel, L. Kamoun “Segmentation d’images par seuillage d’histogramme application a l’analyse des melanomes”, JTEA 2002, 21-22-23 mars 2002.

C. Yu, Canny Edge Detection and Hough transform, Indiana University, 2010.

I. khanfir kallel, contribution a l’identification d’individus par l’iris, Ph.D. Thesis, electrical departement, University of Sfax, 2010.

I. khanfir kallel, D. sallemi masmoudi and N. derbel, A New Iris Extraction Technique For Person Authentication Applications, Third International Conference on Systems, Signals & Devices pp. 21-24, 2005.

http://www.mathworks.com/help/toolbox/images/f11-14011.html.

M. Shamsi, P.B. Saad, S.B. Ibrahim, A.R. Kenari, Fast algorithm for Iris localization using Daugman circular integro-differential operator, International Conference of Soft Computing and Pattern Recognition, pp 393 – 398, 2009.

A. M. Gómez Zúñiga, A Fast and Robust Approach for Iris Segmentation, Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, (Page: 162 Year of Publication: 2008 ISBN: 978-3-540-72848-1).

K. Nguyen, C. Fookes, and S. Sridharan, Fusing shrinking and expanding active contour models for robust iris segmentation,” 10th International Conference on Information Science, Signal Processing and their Applications, pp.185-188, n. 9, 2010.

A. jarjes, K. Wang and G. J .Mohammed, GVF snake-based method for accurate pupil contour detection, Information Technology Journal, vol. 9, n. 2, 2010.

P.V.C. Hough, Method and means for recognizing complex patterns, United State Patent 3069654, 1962.

H. Proenc¸a and L. A. Alexandre, A noisy iris image database, Technical report, university of Beira Interior, Departement of computer science, 2005.

A. Muron and J. Pospisil. The human iris structure and its usages, In Acta Univ. Palacki, Phisica, vol. 39, pp. 87–95, 2000.

H. Proenc¸a and L.A. Alexandre, Iris segmentation methodology for non-cooperative recognition, IEEE proceedings Vision, Image &signal processing, vol 153, pp. 199-205, 2006.

E. Nadernajed, Edge detection techniques: evaluations and comparisons, Applied Mathematical Sciences, vol. 2, 2008.

Y.K. Lai, S. M. Lee, Dynamic Gamma-Correction Algorithm for Improving Color LCD Systems, IEEE International Conference on Consumer Electronics (ICCE), pp. 811 – 812, 2011.

F. Devernay, A Non maxima suppression method for edge detection with sub-pixel accuracy, INRIA Research Rep. 2724, Sophia Antipolis, 1995.

Hong S., N. A. Hazanchuk, Adaptive Edge Detection for Real-Time Video Processing using FPGAs, Global Signal Processing, 2004.

W. aydi, N. Masmoudi and L. Kamoun, Improved Masek approach for iris localization, 23 international conference on microelectronics,pp.1-5, 2011.

Somayeh Ayoobi, Hossein Pourghassem, Optimum Feature Extraction with Wavelet Transform for Iris Recognition, pp. 1996-2002, 2012.

Thiyaneswaran, B., Padma, S., Human authorization using wavelet and tensor object analysis of the iris biometrics, (2012) International Review on Computers and Software (IRECOS), 7 (6), pp. 3047-305.

Aravinth, J., Valarmathy, S., Score-level fusion technique for multi-modal biometric recognition using ABC-based neural network, (2013) International Review on Computers and Software (IRECOS), 8 (8), pp. 1889-190.


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