Journal of Signal Processing Systems

, Volume 81, Issue 1, pp 111–128 | Cite as

Iris Recognition using Robust Localization and Nonsubsampled Contourlet Based Features

  • Sirvan Khalighi
  • Fatemeh Pak
  • Parisa Tirdad
  • Urbano Nunes
Article

Abstract

The conventional iris recognition methods do not perform well for the datasets where the eye image may contain nonideal data such as specular reflection, off-angle view, eyelid, eyelashes and other artifacts. This paper gives contributions for a reliable iris recognition method using a new scale-, shift- and rotation-invariant feature-extraction method in time-frequency and spatial domains. Indeed, a 2-level nonsubsampled contourlet transform (NSCT) is applied on the normalized iris images and a gray level co-occurrence matrix (GLCM) with 3 different orientations is computed on both spatial image and NSCT frequency subbands. Moreover, the effect of the occluded parts is reduced by performing an iris localization algorithm followed by a four regions of interest (ROI) selection. The extracted feature set is transformed and normalized to reduce the effect of extreme values in the feature vector. Next, significant features for iris recognition are selected by a two-step method composed by a filtering stage and wrapper based selection. Finally, the selected feature set is classified using support vector machine (SVM). The proposed iris identification method was tested on the public iris datasets CASIA Ver.1 and CASIA Ver.4-lamp showing a state-of-the-art performance.

Keywords

Gray level co-occurrence matrix Iris recognition Nonsubsampled contourlet transform Feature selection 

References

  1. 1.
    Flom, L., & Safir, A. (Feb. 3 1987). Iris recognition system. U.S. Patent 4 641 349.Google Scholar
  2. 2.
    Jan, F., Usman, I., & Agha, S. (2012). Iris localization in frontal eye images for less constrained iris recognition systems. Digital Signal Processing, 22(6), 971–986. doi:10.1016/j.dsp.2012.06.001.CrossRefGoogle Scholar
  3. 3.
    Daugman, J. G. (1993). High confidence visual recognition of persons by a test of statistical independence. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(11), 1148–1161. doi:10.1109/34.244676.CrossRefGoogle Scholar
  4. 4.
    Wildes, R. P. Iris recognition: An emerging biometric technology. In Proceedings of the IEEE, Sep 1997 (Vol. 85, pp. 1348–1363, Vol. 9). doi:10.1109/5.628669.
  5. 5.
    Ahamed, A., & Bhuiyan, M. I. H. Low Complexity Iris Recognition using Curvelet Transform. In International Conference on Informatics, Electronics & Vision (ICIEV), 2012 (pp. 548–553).Google Scholar
  6. 6.
    Farouk, R. M. (2011). Iris recognition based on elastic graph matching and Gabor wavelets. Computer Vision and Image Understanding, 115(8), 1239–1244. doi:10.1016/j.cviu.2011.04.002.CrossRefGoogle Scholar
  7. 7.
    Poursaberi, A., & Araabi, B. N. (2007). Iris recognition for partially occluded images: methodology and sensitivity analysis. Eurasip Journal on Advances in Signal Processing. doi:10.1155/2007/36751.Google Scholar
  8. 8.
    Roy, K., Bhattacharya, P., & Suen, C. Y. (2011). Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Engineering Applications of Artificial Intelligence, 24(3), 458–475. doi:10.1016/j.engappai.2010.06.014.CrossRefGoogle Scholar
  9. 9.
    Roy, K., Bhattacharya, P., & Suen, C. Y. (2011). Iris segmentation using variational level set method. Optics and Lasers in Engineering, 49(4), 578–588. doi:10.1016/j.optlaseng.2010.09.011.CrossRefGoogle Scholar
  10. 10.
    Szewczyk, R., Grabowski, K., Napieralska, M., Sankowski, W., Zubert, M., & Napieralski, A. (2012). A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform. Pattern Recognition Letters, 33(8), 1019–1026. doi:10.1016/j.patrec.2011.08.018.CrossRefGoogle Scholar
  11. 11.
    Jan, F., Usman, I., & Agha, S. (2013). Reliable iris localization using Hough transform, histogram-bisection, and eccentricity. Signal Processing, 93(1), 230–241. doi:10.1016/j.sigpro.2012.07.033.CrossRefGoogle Scholar
  12. 12.
    Labati, R. D., & Scotti, F. (2010). Noisy iris segmentation with boundary regularization and reflections removal. Image and Vision Computing, 28(2), 270–277. doi:10.1016/j.imavis.2009.05.004.CrossRefGoogle Scholar
  13. 13.
    Jeong, D. S., Hwang, J. W., Kang, B. J., Park, K. R., Won, C. S., Park, D. K., et al. (2010). A new iris segmentation method for non-ideal iris images. Image and Vision Computing, 28(2), 254–260. doi:10.1016/j.imavis.2009.04.001.CrossRefGoogle Scholar
  14. 14.
    Li, P. H., Liu, X. M., Xiao, L. J., & Song, Q. (2010). Robust and accurate iris segmentation in very noisy iris images. Image and Vision Computing, 28(2), 246–253. doi:10.1016/j.imavis.2009.04.010.CrossRefGoogle Scholar
  15. 15.
    Belcher, C., & Du, Y. Z. (2009). Region-based SIFT approach to iris recognition. Optics and Lasers in Engineering, 47(1), 139–147. doi:10.1016/j.optlaseng.2008.07.004.CrossRefGoogle Scholar
  16. 16.
    CASIA Iris Database. http://www.cbsr.ia.ac.cn/english/Databases.asp, 5 May 2013.
  17. 17.
    Proenca, H., Filipe, S., Santos, R., Oliveira, J., & Alexandre, L. A. (2010). The UBIRIS.v2: A Database of Visible Wavelength Iris Images Captured On-the-Move and At-a-Distance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(8), 1529–1535. doi:10.1109/Tpami.2009.66.CrossRefGoogle Scholar
  18. 18.
    Kim, J., Cho, S. W., Choi, J., & Marks, R. J. (2004). Iris recognition using wavelet features. Journal of Vlsi Signal Processing Systems for Signal Image and Video Technology, 38(2), 147–156. doi:10.1023/B:Vlsi.0000040426.72253.B1.CrossRefGoogle Scholar
  19. 19.
    Chen, C. H., & Chu, C. T. (2009). High performance iris recognition based on 1-D circular feature extraction and PSO-PNN classifier. Expert Systems with Applications, 36(7), 10351–10356. doi:10.1016/j.eswa.2009.01.033.CrossRefGoogle Scholar
  20. 20.
    He, Z. F., Tan, T. N., Sun, Z. A., & Qiu, X. C. (2009). Toward accurate and fast iris segmentation for iris biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(9), 1670–1684. doi:10.1109/Tpami.2008.183.CrossRefGoogle Scholar
  21. 21.
    Tsai, C. C., Lin, H. Y., Taur, J., & Tao, C. W. (2012). Iris recognition using possibilistic fuzzy matching on local features. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 42(1), 150–162. doi:10.1109/Tsmcb.2011.2163817.CrossRefGoogle Scholar
  22. 22.
    Do, M. N., & Vetterli, M. (2005). The contourlet transform: an efficient directional multiresolution image representation. IEEE Transactions on Image Processing, 14, 2091–2106.CrossRefGoogle Scholar
  23. 23.
    Li, M. Y., Jiang, M. Y., Han, M., & Yang, M. Q. Iris Recognition Based on a Novel Multiresolution Analysis Framework. In 2010 IEEE International Conference on Image Processing, 2010 (pp. 4101-4104). doi:10.1109/Icip.2010.5652298.
  24. 24.
    da Cunha, A. L., Zhou, J. P., & Do, M. N. (2006). The nonsubsampled contourlet transform: theory, design, and applications. IEEE Transactions on Image Processing, 15(10), 3089–3101. doi:10.1109/Tip.2006.877507.CrossRefGoogle Scholar
  25. 25.
    Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural Features of Image Classification. IEEE Transaction on Systems, Man and Cybernetics, 3(6), 610–621.Google Scholar
  26. 26.
    Khalighi, S., Tirdad, P., Pak, F., & Nunes, U. Shift and Rotation Invariant Iris Feature Extraction Based on Non-subsampled Countourlet Transform and GLCM. In In proceeding of International Conference on Pattern Recognition Applications and Methods, Vilamoura, Portugal, 2012. Google Scholar
  27. 27.
    Shah, S., & Ross, A. (2009). Iris segmentation using geodesic active contours. IEEE Transactions on Information Forensics and Security, 4(4), 824–836. doi:10.1109/Tifs.2009.2033225.CrossRefGoogle Scholar
  28. 28.
    Masek, L. (2003). Recognition of Human Iris Patterns for Biometric Identification. The School of Computer Science and Software Engineering the University of Western Australia.Google Scholar
  29. 29.
    Po, D. D. Y., & Do, M. N. (2006). Directional multiscale modeling of images using the contourlet transform. IEEE Transactions on Image Processing, 15(6), 1610–1620. doi:10.1109/Tip.2006.873450.CrossRefGoogle Scholar
  30. 30.
    Do, M. N., & Vetterli, M. Pyramidal directional filter banks and curvelets. In International Conference on Image Processing, 2001 (Vol. II, pp. 158-161).Google Scholar
  31. 31.
    Soh, L. K., & Tsatsoulis, C. (1999). Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Transactions on Geoscience and Remote Sensing, 37(2), 780–795. doi:10.1109/36.752194.CrossRefGoogle Scholar
  32. 32.
    Haralick, R., & Shapiro, L. (1992). Computer and Robot Vision (Vol. 1): Addison-Wesley.Google Scholar
  33. 33.
    Clausi, D. A. (2002). An analysis of co-occurrence texture statistics as a function of grey level quantization. Canadian Journal of Remote Sensing, 28(1), 45–62.CrossRefGoogle Scholar
  34. 34.
    Becq, G., Charbonnier, S., Chapotot, F., Buguet, A., Bourdon, L., & Baconnier, P. (2005). Comparison between five classifiers for automatic scoring of human sleep recordings. Classification and Clustering for Knowledge Discovery, 4, 113–127.Google Scholar
  35. 35.
    Aksoy, S., & Haralick, R. M. (2001). Feature normalization and likelihood-based similarity measures for image retrieval. Pattern Recognition Letters, 22(5), 563–582. doi:10.1016/S0167-8655(00)00112-4.CrossRefGoogle Scholar
  36. 36.
    Khushaba, R. N., Al-Ani, A., & Al-Jumaily, A. (2011). Feature subset selection using differential evolution and a statistical repair mechanism. Expert Systems with Applications, 38(9), 11515–11526. doi:10.1016/j.eswa.2011.03.028.CrossRefGoogle Scholar
  37. 37.
    Peng, H. C., Long, F. H., & Ding, C. (2005). Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238.CrossRefGoogle Scholar
  38. 38.
    Whitney, A. W. (1971). A Direct Method of Nonparametric Measurement Selection. IEEE Transactions on Computers, 20(9).Google Scholar
  39. 39.
    Pudil, P., Novovicova, J., & Kittler, J. (1994). Floating search methods in feature-selection. Pattern Recognition Letters, 15(11), 1119–1125. doi:10.1016/0167-8655(94)90127-9.CrossRefGoogle Scholar
  40. 40.
    Burges, C. J. C. (1998). A tutorial on Support Vector Machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. doi:10.1023/A:1009715923555.CrossRefGoogle Scholar
  41. 41.
    Canu, S., Grandvalet, Y., Guigue, V., & Rakotomamonjy, A. (2005). SVM and Kernel methods Matlab toolbox.Google Scholar
  42. 42.
    Daugman, J. (2007). New methods in iris recognition. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 37(5), 1167–1175. doi:10.1109/Tsmcb.2607.903540.CrossRefGoogle Scholar
  43. 43.
    Basit, A., & Javed, M. Y. (2007). Localization of iris in gray scale images using intensity gradient. Optics and Lasers in Engineering, 45(12), 1107–1114. doi:10.1016/j.optlaseng.2007.06.006.CrossRefGoogle Scholar
  44. 44.
    Ibrahim, M. T., Khan, T. M., Khan, S. A., Khan, M. A., & Guan, L. (2012). Iris localization using local histogram and other image statistics. Optics and Lasers in Engineering, 50(5), 645–654. doi:10.1016/j.optlaseng.2011.11.008.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Sirvan Khalighi
    • 1
  • Fatemeh Pak
    • 2
  • Parisa Tirdad
    • 3
  • Urbano Nunes
    • 1
  1. 1.Institute for Systems and Robotics (ISR-UC)Electrical and Computer Engineering Department, University of CoimbraCoimbraPortugal
  2. 2.Department of Computer and Information Technology EngineeringAzad University of QazvinQazvinIran
  3. 3.Department of Information Systems EngineeringConcordia UniversityMontrealCanada

Personalised recommendations