AASRI Procedia 9 ( 2014 ) 2 – 7

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2212-6716 © 2014 The Authors. Published by Elsevier B. V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).Peer-review under responsibility of Scientific Committee of American Applied Science Research Institutedoi: 10.1016/j.aasri.2014.09.002

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3 Izem Hamouchene and Saliha Aouat / AASRI Procedia 9 ( 2014 ) 2 – 7

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4 Izem Hamouchene and Saliha Aouat / AASRI Procedia 9 ( 2014 ) 2 – 7

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5 Izem Hamouchene and Saliha Aouat / AASRI Procedia 9 ( 2014 ) 2 – 7

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6 Izem Hamouchene and Saliha Aouat / AASRI Procedia 9 ( 2014 ) 2 – 7

WM1 airis i

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7 Izem Hamouchene and Saliha Aouat / AASRI Procedia 9 ( 2014 ) 2 – 7

58.75% and the NBP method is 76.25%.We can notice, from the results, that the NBP method is better than the classical LBP method.Because in fact, the neighborhood of the central pixel is thresholded by its value using the LBP method.So, LBP get the relationship between each neighbor and the central pixel.However, the NBP method describes each pixel by the relationship of its neighborhoods.Experimental results demonstrate that it is more interesting to capture the relative pertinent information between the neighbors.Thus, the results have shown the robustness and the efficiency of the proposed NBP method.

4. Conclusion

In this paper, we have proposed a new IRS system. This system used a new feature extraction method NBP. The NBP method extracts the relative connection between neighbors of pixels. Each neighbor of each pixel is thresholded by the next neighbor and encoded.After that, the NBP image is decomposed into several blocks. The mean of each block is calculated. Then, the variations of the meanare encoded. The resulted binary matrix is used as a feature descriptor of the iris. In the experimentation, the CASIA iris database is used. Good performance has been obtained for the proposed IRS. We can summarize that the proposed NBP method is interesting since it gets the relative relevant information between the neighbors of pixels.In future works, we will study the combination of the NBP method with other approaches like Gabor transform.

References

[1] B.Fang,Y.Y.Tang, “Elasticregistrationforretinal images based on reconstructedvascular trees”, IEEE Trans. on BiomedicalEngineering,vol. 53, no. 6, pp. 1183–1187, June 2006. [2] R. Szewczyk, K. Grabowski,M. Napieralska, W. Sankowski, M. Zubert, A. Napieralski, “A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform”, Pattern RecognitionLetters, Volume 33, Issue 8, Pages 1019–1026,2012. [3] Shu-Ren Zhou, Jian-Ping Yin, Jian-Ming Zhang,“Local binary pattern (LBP) and local phase quantization (LBQ) based on Gabor filter for face representation”. Neurocomputing, Vol.116 ,pp. 260-264, 2013. [4] J.G. Daugman, “High Confidence Visual Recognition of Persons by a Test of Statistical Independence”, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp.1148-1161,1993. [5] J.Daugman,C.Downing, “Epigenetic randomness, complexity, and singularity of human iris patterns”, Proceedings of the Royal Society (London) Series B: Biological Sciences,vol. 268, pp.1737–1740. 2001. [6] Abidin, Z.Z.; Manaf, M.; Shibghatullah, A.S.; Anawar, S.; Ahmad, R., "Feature extraction from epigenetic traits using edge detection in iris recognition system," IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp.145-149, 2013. [7]J. Daugman, “How iris recognition works”, IEEE Trans. on Circuits and Systems for Video Technology, vol. 14, no.1, pp. 21-30,2004. [8] J. Daugman, “New methods in iris recognition”, IEEE Trans. on System, Man, and Cybernetics, vol. 37, no. 5, pp. 1167-1175, 2007. [9] J. Daugman “The importance of being random: statistical principles of iris recognition”, Pattern recognition, vol. 36, no.2, pp. 279-291.2003. [10]CASIA iris image database (v1.0), The National Laboratory of Pattern Recognition (NLPR), Institute of Automation, Chinese Academy of Sciences (CAS), 2006. [11] Hamouchene I. Aouat S, Lacheheb H. “New segmentation architecture for texture matching using the LBP method”. IEEE Technically Co-Sponsored SAI Conference, London UK, 2013. [12] L. Flom and A. Safir, “Iris Recognition System”, U.S. Patent 4,641,349, 1987. [13] Kevin W. Bowyer, Karen P. Hollingsworth, Patrick J. Flynn, “A Survey of Iris Biometrics Research: 2008–2010”, Handbook of Iris Recognition, pp. 15-54, 2013.