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Efficient approach for iris recognition

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Abstract

Iris texture is a natural password that has great advantages such as variability, stability, unique features for each person, and its importance in the security field. This makes an iris recognition system upper of other biometric methods used for human identification. Recent science is interested to develop intelligent systems able to identify persons based on the texture of their iris. We proposed a new feature extraction method based on local and directional texture information. The proposed feature extraction method gets both local and global relevant information and faster than commonly used method. In the experimental parts, our system is compared to other famous and recent iris recognition systems using CASIA iris dataset. Experiments demonstrate that the proposed system gives better recognition rate (99.96 %) compared to other systems.

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References

  1. Richards, W., Andres, P.: Texture matching. Kybernetik 16, 155–162 (1974)

    Article  Google Scholar 

  2. Hamouchene, I., Aouat, S.: Overview of texture analysis. Int. J. Comput. Vis. Image Process. IJCVIP 4(2), 20 (2014)

    Google Scholar 

  3. Kaya, Y., Kayci, L., Tekin, R., Faruk Erturul, O.: Evaluation of texture features for automatic detecting butterfly species using extreme learning machine. J. Exp. Theor. Artif. Intell. 26(2), 267–281 (2014)

    Article  Google Scholar 

  4. Fang, B., Tang, Y.Y.: Elastic registration for retinal images based on reconstructed vascular trees. IEEE Trans. Biomed. Eng. 53(6), 1183–1187 (2006)

    Article  Google Scholar 

  5. Zhang, T., Fang, B., Tang, Y.Y., He, G., Wen, J.: Topology preserving non-negative matrix factorization for face recognition. IEEE Trans. Image Process. 17(4), 574–584 (2008)

    Article  MathSciNet  Google Scholar 

  6. Julian, A.: Biometrics: Advanced Identity Verification, The Complete Guide. Springer, Berlin (2000)

    Google Scholar 

  7. Jain, A.K., Ross, A., Prabhakar, S.: Fingerprint matching using minutiae and texture features. Int. Conf. Image Process. 3, 282–285 (2001)

    Google Scholar 

  8. Connolly, J., Granger, E., Sabourin, R.: An adaptive classification system for video-based face recognition. Inf. Sci. 192, 50–70 (2010)

    Article  Google Scholar 

  9. Sanchez-Avila, C., Snchez-Reillo, R.: Two different approaches for iris recognition using Gabor filters and multiscale zero-crossing representation. Pattern Recognit. 38(2), 231–240 (2005)

    Article  Google Scholar 

  10. Kovari, B., Charaf, H.: A study on the consistency and significance of local features in off-line signature verification. Pattern Recognit. Lett. 34(3), 247–255 (2013)

    Article  Google Scholar 

  11. Dahl, G.E., Yu, D., Deng, L., Acero, A.: Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition. IEEE Trans Audio Speech Lang Process 20(1), 30–42 (2012)

    Article  Google Scholar 

  12. Zhang, D.: Biometrics technologies and applications. In: Proceedings of international conference on image and graphics, pp. 42–49. Tianjing, China (2000)

  13. loicTisse, Ch., Martin, L., Torres, L., Robert, M.: Person identification technique using human iris recognition. In: The 15th International Conference on Vision Interface, pp. 294–299 (2002)

  14. Park, H.-A., Park, K.R.: Iris recognition based on score level fusion by using SVM. Pattern Recognit. Lett. 28(15), 2019–2028 (2007)

    Article  Google Scholar 

  15. Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., Napieralski, A.: A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognit. Lett. 33(8), 1019–1026 (2012)

    Article  Google Scholar 

  16. Wildes, P.R.: An emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)

    Article  Google Scholar 

  17. Daugman, J.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)

  18. Wildes, R.: A machine-vision system for iris recognition. Mach. Vis. Appl. 9, 1–8 (1996)

  19. Hallinan, P.W.: Recognizing human eyes. Geom. Methods Comput. Vis. 1570, 214–226 (1991)

    Article  Google Scholar 

  20. Flom, L., Safir, A.: Iris Recognition System. U.S. Patent,4, 641–349 (1987)

  21. Bowyer, K.W., Hollingsworth, K.P., Patrick J., Flynn, A.: Survey of Iris Biometrics Research, Handbook of Iris Recognition, pp. 15–54 (2013)

  22. Yu, L., Zhang, D., Wang, K.: The relative distance of key point based iris recognition. Pattern Recognit. 40(2), 423–430 (2007)

    Article  MATH  Google Scholar 

  23. Wildes, R.: Iris recognition: an emerging biometric technology. Proc. IEEE 46(4), 1185–1188 (1998)

    Google Scholar 

  24. Mohamed, M.A., Abou-Elsoud, M.E., Eid, M.M.: An efficient algorithm in extracting human iris morphological features. In: IEEE International Conference on Networking and Media Convergence (ICNM), pp. 146–150 (2009)

  25. Daugman, J.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)

    Article  Google Scholar 

  26. Boles, W., Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185–1188 (1998)

    Article  Google Scholar 

  27. Ojala, T., Pietikinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognit. 29, 51–59 (1996)

    Article  Google Scholar 

  28. CASIA iris image database (v1.0): The National Laboratory of Pattern Recognition (NLPR). Institute of Automation, Chinese Academy of Sciences (CAS) (2006)

  29. Masek, L.: Recognition of Human Iris Patterns for Biometric Identification, The School of Computer Science and Software Engineering. The University of Western Australia, Crawley, WA (2003)

    Google Scholar 

  30. Han, W.Y., Chen, W.K., Lee, Y.P., Wu, K., Lee, J.C.: Iris recognition based on local mean decomposition. Appl. Math. Inf. Sci. 8(1), 217–222 (2014)

    Article  Google Scholar 

  31. Rai, H., Yadav, A.: Iris recognition using combined support vector machine and Hamming distance approach. Expert Syst. Appl. 41(2), 588–593 (2014)

    Article  Google Scholar 

  32. Hamouchene, I., Aouat, S.: A new texture analysis approach for iris recognition. AASRI Conference on Circuit and Signal Processing (CSP 2014) 9, 27 (2014)

  33. Hamouchene, I., Aouat, S.: A cognitive approach for texture analysis using neighbors-based binary patterns. In: IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing (ICCI*CC), pp. 94–99 (2014)

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Authors and Affiliations

  1. LRIA Laboratory, Computer Science Department, USTHB University, Algiers, Algeria

    Izem Hamouchene & Saliha Aouat

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  1. Izem Hamouchene
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  2. Saliha Aouat
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Correspondence to Izem Hamouchene.

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Hamouchene, I., Aouat, S. Efficient approach for iris recognition. SIViP 10, 1361–1367 (2016). https://doi.org/10.1007/s11760-016-0900-y

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  • DOI: https://doi.org/10.1007/s11760-016-0900-y

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