Iris Segmentation

  • Authors

    • Anis Farihan Mat Raffei
    • Rohayanti Hassan
    • Shahreen Kasim
    • Hishamudin Asmuni
    • Asraful Syifaa’ Ahmad
    • Rahmat Hidayat
    • Ansari Saleh Ahmar
    2018-03-10
    https://doi.org/10.14419/ijet.v7i2.5.13956
  • Iris recognition, Iris Segmentation

  • Abstract

    The quality of eye image data become degraded particularly when the image is taken in the non-cooperative acquisition environment such as under visible wavelength illumination. Consequently, this environmental condition may lead to noisy eye images, incorrect localization of limbic and pupillary boundaries and eventually degrade the performance of iris recognition system. Hence, this study has compared several segmentation methods to address the abovementioned issues. The results show that Circular Hough transform method is the best segmentation method with the best overall accuracy, error rate and decidability index that more tolerant to ‘noise’ such as reflection.

     

     

  • References

    1. [1] H. Proença, S. Filipe, R. Santos, J. Oliveira, and L. A. Alexandre, “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, vol. 32, no. 8, pp. 1529–1535, 2010.

      [2] A. Radman, K. Jumari, and N. Zainal, “Iris segmentation: A review and research issues,†in Communications in Computer and Information Science, 2011, vol. 179 CCIS, no. PART 1, pp. 698–708.

      [3] Z. Zhou, P. Yao, Z. Zhuang, and J. Li, “A robust algorithm for iris localization based on radial symmetry and circular integro differential operator,†in Proceedings of the 2011 6th IEEE Conference on Industrial Electronics and Applications, ICIEA 2011, 2011, pp. 1742–1745.

      [4] T. Tan, Z. He, and Z. Sun, “Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition,†Image and Vision Computing, vol. 28, no. 2, pp. 223–230, Feb. 2010.

      [5] M. Shamsi, P. B. Saad, S. B. Ibrahim, and A. R. Kenari, “Fast algorithm for iris localization using Daugman circular integro differential operator,†in SoCPaR 2009 - Soft Computing and Pattern Recognition, 2009, pp. 393–398.

      [6] X. Liu, P. Li, and Q. Song, “Eyelid localization in iris images captured in less constrained environment,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2009, vol. 5558 LNCS, pp. 1140–1149.

      [7] W. Li, M. Xie, and P. Yu, “A Fast and Robust Method for Iris Localization Using Light Spot,†Journal of Computational Information Systems, vol. 7, no. 13, pp. 4619–4627, 2011.

      [8] H. Li, Z. Sun, and T. Tan, “Accurate Iris Localization Using Contour Segments,†International Conference on Pattern Recognition, no. Icpr, pp. 3398–3401, 2012.

      [9] Y.-H. Li and M. Savvides, “An Automatic Iris Occlusion Estimation Method Based on High Dimensional Density Estimation,†IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 4, pp. 1–1, 2012.

      [10] A. Ghanizadeh, A. A. Abarghouei, S. Sinaie, P. Saad, and S. M. Shamsuddin, “Iris segmentation using an edge detector based on fuzzy sets theory and cellular learning automata,†Applied Optics, vol. 50, no. 19, p. 3191, Jul. 2011.

      [11] A. E. Yahya and M. J. Nordin, “Improving iris segmentation by specular reflections removable,†in Proceedings 2010 International Symposium on Information Technology - Visual Informatics, ITSim’10, 2010, vol. 1, pp. 1–3.

      [12] S. S. S, K. N. B. Murthy, S. Natarajan, M. R, and A. S, “Feed Forward Neural Network Based Eye Localization and Recognition Using Hough Transform,†IJACSA International Journal of Advanced Computer Science and Applications, vol. 2, no. 3, pp. 104–109, 2011.

      [13] Y. Chen, M. Adjouadi, C. Han, J. Wang, A. Barreto, N. Rishe, and J. Andrian, “A highly accurate and computationally efficient approach for unconstrained iris segmentation,†Image and Vision Computing, vol. 28, no. 2, pp. 261–269, Feb. 2010.

      [14] A. Bendale, A. Nigam, S. Prakash, and P. Gupta, “Iris segmentation using improved Hough transform,†8th International Conference on Emerging Intelligent Computing Technology and Applications, ICIC 2012, vol. 304 CCIS. Springer, Berlin, Heidelberg, pp. 408–415, 2012.

      [15] D. M. Rankin, B. W. Scotney, P. J. Morrow, D. R. McDowell, and B. K. Pierscionek, “Dynamic iris biometry: a technique for enhanced identification.,†BMC research notes, vol. 3, no. 1, p. 182, Jul. 2010.

      [16] N. K. Mahadeo and N. Bhattacharjee, “An efficient and accurate iris segmentation technique,†in DICTA 2009 - Digital Image Computing: Techniques and Applications, 2009, pp. 347–352.

      [17] M. Adam, F. Rossant, F. Amiel, B. Mikovicova, and T. Ea, “Reliable eyelid localization for iris recognition,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2008, vol. 5259 LNCS, pp. 1062–1070.

      [18] H. Proença, “Non-cooperative iris recognition: Issues and trends,†in Signal Processing Conference, 2011.

      [19] H. Demirel and G. Anbarjafari, “Iris recognition system using combined histogram statistics,†2008 23rd International Symposium on Computer and Information Sciences, pp. 1–4, Oct. 2008.

      [20] B. Kaur, G. A. P. Anil, and E. M. M. E. In, “Comparative Study of Differentedge Detection Techniques,†in International Journal of Engineering Science and Technology (IJEST), 2011, pp. 1927–1935.

      [21] V. G. Narendra and K. S. Hareesh, “Study and comparison of various image edge detection techniques used in quality inspection and evaluation of agricultural and food products by computer vision,†Int J Agric & Biol Eng Open Access at Int J Agric & Biol Eng, vol. 4, no. 2, pp. 83–90, 2011.

      [22] M. Juneja and P. S. Sandhu, “Performance evaluation of edge detection techniques for images in spatial domain,†International Journal of Computer Theory and Engineering, vol. 1, no. 5, pp. 614–622, 2009.

      [23] T. H. Min and R. H. Park, “Eyelid and eyelash detection method in the normalized iris image using the parabolic Hough model and Otsu’s thresholding method,†Pattern Recognition Letters, vol. 30, no. 12, pp. 1138–1143, Sep. 2009.

      [24] Y. K. Jang, B. J. Kang, and K. R. Park, “A study on eyelid localization considering image focus for iris recognition,†Pattern Recognition Letters, vol. 29, no. 11, pp. 1698–1704, Aug. 2008.

      [25] S. Dalal and T. Sahoo, “A Selective Feature Matching Approach for Iris Recognition,†International Journal of Computer Applications, vol. 41, no. 20, pp. 975–8887, 2012.

      [26] F. Sayeed, M. Hanmandlu, A. Q. Ansari, and S. Vasikarla, “Iris recognition using segmental Euclidean distances,†in Proceedings - 2011 8th International Conference on Information Technology: New Generations, ITNG 2011, 2010, pp. 520–525.

      [27] L. Yu, D. Zhang, and K. Wang, “The relative distance of key point based iris recognition,†Pattern Recognition, vol. 40, no. 2, pp. 423–430, Feb. 2007.

      [28] K. P. Hollingsworth, K. W. Bowyer, and P. J. Flynn, “Improved iris recognition through fusion of hamming distance and fragile bit distance,†IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 12, pp. 2465–2476, Dec. 2011.

      [29] R. P. Broussard, L. R. Kennell, R. W. Ives, and R. N. Rakvic, “An artificial neural network based matching metric for iris identification,†2008, vol. 6812, p. 68120S.

  • Downloads

  • How to Cite

    Farihan Mat Raffei, A., Hassan, R., Kasim, S., Asmuni, H., Syifaa’ Ahmad, A., Hidayat, R., & Saleh Ahmar, A. (2018). Iris Segmentation. International Journal of Engineering & Technology, 7(2.5), 77-83. https://doi.org/10.14419/ijet.v7i2.5.13956

    Received date: 2018-06-09

    Accepted date: 2018-06-09

    Published date: 2018-03-10