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Efficient Small Template Iris Recognition System Using Wavelet Transform
Mohammed A. M. Abdullah, F. H. A. Al-Dulaimi, Waleed Al-Nuaimy, Ali Al-Ataby
Pages - 16 - 27     |    Revised - 31-03-2011     |    Published - 04-04-2011
Volume - 5   Issue - 1    |    Publication Date - March / April 2011   Table of Contents
Iris Recognition, Feature Extraction, Wavelet Transform
Iris recognition is known as an inherently reliable biometric technique for human identification. Feature extraction is a crucial step in iris recognition, and the trend nowadays is to reduce the size of the extracted features. Special efforts have been applied in order to obtain low templates size and fast verification algorithms. These efforts are intended to enable a human authentication in small embedded systems, such as an Integrated Circuit smart card. In this paper, an effective eyelids removing method, based on masking the iris, has been applied. Moreover, an efficient iris recognition encoding algorithm has been employed. Different combination of wavelet coefficients which quantized with multiple quantization levels are used and the best wavelet coefficients and quantization levels are determined. The system is based on an empirical analysis of CASIA iris database images. Experimental results show that this algorithm is efficient and gives promising results of False Accept Ratio (FAR) = 0% and False Reject Ratio (FRR) = 1% with a template size of only 364 bits.
CITED BY (8)  
1 Abdullah, M. A., Chambers, J. A., Woo, W. L., & Dlay, S. S. (2015). Iris Biometric: Is the Near-Infrared Spectrum always the Best?.
2 Al-Zubi, R. T., Darabkh, K. A., & Al-Zubi, N. (2015). Effect of Eyelid and Eyelash Occlusions on a Practical Iris Recognition System: Analysis and Solution. International Journal of Pattern Recognition and Artificial Intelligence, 29(08), 1556016.
3 Vera, D. F., Cadena, D. M., & Ramirez, J. M. (2015, September). Iris recognition algorithm on BeagleBone Black. In Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on (Vol. 1, pp. 282-286). IEEE.
4 Abdullah, M. A., Dlay, S. S., & Woo, W. L. (2014, November). Fast and Accurate Pupil Isolation Based on Morphology and Active Contour. In The 4th International conference on Signla, Image Processing and Applications (pp. 418-420).
5 Abdullah, M., Dlay, S. S., & Woo, W. L. (2014, October). Fast and accurate method for complete iris segmentation with active contour and morphology. In Imaging Systems and Techniques (IST), 2014 IEEE International Conference on (pp. 123-128). IEEE.
6 Al-Zubi, R. T., Darabkh, K. A., & Jararweh, Y. I. (2014). A Powerful Yet Efficient Iris Recognition Based on Local Binary Quantization. Information Technology And Control, 43(3), 244-251.
7 Zhang, Y., Laurikkala, J., & Juhola, M. (2014). Biometric verification of a subject with eye movements, with special reference to temporal variability in saccades between a subject’s measurements. International Journal of Biometrics, 6(1), 75-94.
8 Zhang, Y., Rasku, J., & Juhola, M. (2012). Biometric verification of subjects using saccade eye movements. International Journal of Biometrics, 4(4), 317-337.
1 Google Scholar 
2 Academic Journals Database 
3 CiteSeerX 
4 refSeek 
5 iSEEK 
6 Libsearch 
7 Bielefeld Academic Search Engine (BASE) 
8 Scribd 
9 WorldCat 
10 SlideShare 
11 PdfSR 
A. Kumar, A. Passi. “Comparison and Combination of Iris Matchers for Reliable Personal Identification”. Computer Vision and Pattern Recognition Workshops, IEEE, 2008
A. Poursaberi,B.N. Araabi. “Iris Recognition for Partially Occluded mages: Methodology and Sensitivity Analysis”. EURASIP Journal on Advances in Signal Processing, 2007(1): 12-14, 2007
A.Mansfield and J.Wayman, “Best practice standards for testing and reporting on biometric device performance”. National Physical Laboratory of UK, 2002
C. Chena, C. Chub. “High Performance Iris Recognition based on 1-D Circular Feature Extraction and PSO–PNN Classifier”. Expert Systems with Applications journal, 36(7): 10351-10356, 2009
Chinese Academy of Sciences, Center of Biometrics and Security Research. Database of 756 Grayscale Eye Images. http://www.cbsr.ia.ac.cn/IrisDatabase.htm
D. Woodward, M. Orlans and T. Higgins. “Biometrics”. McGraw-Hill, Berkeley, California, pp. 15-21 (2002)
Department of Computer Science, University of Beira Interior, Database of eye images. Version 1.0, 2004. http://iris.di.ubi.pt/
H. Huang, G. Hu. “Iris Recognition Based on Adjustable Scale Wavelet Transform”. 27th Annual International Conference of the Engineering in Medicine and Biology Society, Shanghai, 2005
H. Huang, P.S. Chiang, J. Liang. “Iris Recognition Using Fourier-Wavelet Features”. 5th International Conference Audio- and Video-Based Biometric Person Authentication, Hilton Rye Town, New York, 2005
H. Proença, A. Alexandre. “Towards noncooperative iris recognition: A classification approach using multiple signatures”. IEEE Transaction on Pattern Analysis, 29(4): 607-612, 2007
H. Xiaofu, S. Pengfei. “Extraction of Complex Wavelet Features for Iris Recognition”. Pattern Recognition, 19th International Conference on Digital Object Identifier, Shanghai, 2008
J. Daugman, ”High confidence visual recognition of persons by a test of statistical independence”. IEEE Transaction on Pattern Analysis, 15(11): 1148-1161, 1993
J. Daugman. “Statistical Richness of Visual Phase Information: Update on Recognizing, Persons by Iris Patterns”. International Journal of Computer Vision, 45(1): 25-38, 2001
J. Kim, S. Cho, R. J. Marks. “Iris Recognition Using Wavelet Features”. The Journal of VLSI Signal Processing, 38(2): 147-156, 2004
K. Dmitry “Iris Recognition: Unwrapping the Iris”. The Connexions Project and Licensed Under the Creative Commons Attribution License, Version 1.3. (2004)
L. Ma. “Personal identification based on iris recognition”. Ph.D. dissertation, Institute of Automation, Chinese Academy of Sciences, Beijing, China, 2003
O. A Alim, M. Sharkas. “Iris Recognition Using Discrete Wavelet Transform and Artificial Neural Networks”. IEEE International Symposium on Micro-Nano Mechatronics and Human Science, Alexandria, 2005
R. Schalkoff. “Pattern Recognition: Statistical, Structural and Neural Approaches”. John Wiley and Sons Inc., pp. 55-63 (2003)
R. Wildes, J. Asmuth, G. Green, S. Hsu, and S. Mcbride. “A System for Automated Iris Recognition”, Proceedings IEEE Workshop on Applications of Computer Vision, Sarasota, FL, USA, 1994
S. Cho, J. Kim. ”Iris Recognition Using LVQ Neural Network”. International conference on signals and electronic systems, Porzan, 2005
S. Hariprasath, V. Mohan. “Biometric Personal Identification Based On Iris Recognition Using Complex Wavelet Transforms”. Proceedings of the 2008 International Conference on Computing, Communication and Networking (ICCCN) IEEE, 2008
S.P. Narote, A.S. Narote, L.M. Waghmare, M.B. Kokare, A.N. Gaikwad. “An Iris Recognition Based on Dual Tree Complex Wavelet Transform”. TENCON IEEE 10th conference, Pune, India, 2007
T. Morav?ík. “An Approach to Iris and Pupil Detection in Eye Image”. XII International PhD Workshop OWD, University of Žilina, Slovakia, 2010
W. Boles, B. Boashash. “A Human Identification Technique Using Images of the Iris and Wavelet Transform”. IEEE Transactions on Signal Processing, 46(4): 1085–1088, 1998
Mr. Mohammed A. M. Abdullah
University of Mosul - Iraq
Dr. F. H. A. Al-Dulaimi
University of Mosul - Iraq
Dr. Waleed Al-Nuaimy
University of Liverpool - United Kingdom
Mr. Ali Al-Ataby
University of Liverpool - United Kingdom

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