Abstract
Iris recognition for eye images acquired in visible wavelength is receiving increasing attention. In visible wavelength environments, there are many factors that may cover or affect the iris region which makes the iris segmentation step more difficult and challenging. In this paper, we propose a novel and fast segmentation algorithm to deal with eye images acquired in visible wavelength environments by considering the color information form multiple color spaces. The various existing color spaces such as RGB, YCbCr, and HSV are analyzed and an appropriate set of color models is selected for the segmentation process. To accurately localize the iris region, a set of convenient techniques are applied to detect and remove the non-iris regions such as pupil, specular reflection, eyelids, and eyelashes. Our experimental results and comparative analysis using the UBIRIS v2 database demonstrate the efficiency of our approach in terms of segmentation accuracy and execution time.
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References
Abdullah, M.A., Dlay, S.S., Woo, W.L., Chambers, J.A.: Robust iris segmentation method based on a new active contour force with a noncircular normalization. IEEE Trans. Syst. Man Cybern. Syst. 47(12), 3128–3141 (2016)
Bao, P., Zhang, L., Wu, X.: Canny edge detection enhancement by scale multiplication. IEEE Trans. Pattern Anal. Mach. Intell. 27(9), 1485–1490 (2005)
Bazrafkan, S., Thavalengal, S., Corcoran, P.: An end to end deep neural network for iris segmentation in unconstrained scenarios. Neural Netw. 106, 79–95 (2018)
Bezerra, C.S., et al.: Robust iris segmentation based on fully convolutional networks and generative adversarial networks. In: 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 281–288. IEEE (2018)
Chen, Y., Wang, W., Zeng, Z., Wang, Y.: An adaptive CNNs technology for robust iris segmentation. IEEE Access 7, 64517–64532 (2019)
Chen, Y., Adjouadi, M., Barreto, A., Rishe, N., Andrian, J.: A computational efficient iris extraction approach in unconstrained environments. In: 2009 IEEE 3rd International Conference on Biometrics: Theory, Applications, and Systems, pp. 1–7. IEEE (2009)
Daugman, J.: How iris recognition works. In: The Essential Guide to Image Processing, pp. 715–739. Elsevier (2009)
Daugman, J.G.: High confidence visual recognition of persons by a test of statistical independence. IEEE Trans. Pattern Anal. Mach. Intell. 15(11), 1148–1161 (1993)
Jain, A.K., Flynn, P., Ross, A.A.: Handbook of Biometrics. Springer, US (2007). https://doi.org/10.1007/978-0-387-71041-9
Masek, L., et al.: Recognition of human iris patterns for biometric identification. Ph.D. thesis, Master’s thesis, University of Western Australia (2003)
Ng, R.Y.F., Tay, Y.H., Mok, K.M.: A review of iris recognition algorithms. In: 2008 International Symposium on Information Technology, vol. 2, pp. 1–7. IEEE (2008)
O’Gorman, L.: Comparing passwords, tokens, and biometrics for user authentication. Proc. IEEE 91(12), 2021–2040 (2003)
Osorio-Roig, D., Rathgeb, C., Gomez-Barrero, M., Morales-González, A., Garea-Llano, E., Busch, C.: Visible wavelength iris segmentation: a multi-class approach using fully convolutional neuronal networks. In: 2018 International Conference of the Biometrics Special Interest Group (BIOSIG), pp. 1–5. IEEE (2018)
Pedersen, S.J.K.: Circular hough transform. Aalborg University, Vision, Graphics, and Interactive Systems, vol. 123, no. 6 (2007)
Phillips, P.J., Bowyer, K.W., Flynn, P.J.: Comments on the casia version 1.0 iris data set. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(10), 1869–1870 (2007)
Proenca, H.: Iris recognition: on the segmentation of degraded images acquired in the visible wavelength. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1502–1516 (2009)
Proença, H.: Ocular biometrics by score-level fusion of disparate experts. IEEE Trans. Image Process. 23(12), 5082–5093 (2014)
Proenca, H., Filipe, S., Santos, R., Oliveira, J., Alexandre, L.A.: The UBIRIS. v2: a database of visible wavelength iris images captured on-the-move and at-a-distance. IEEE Trans. Pattern Anal. Mach. Intell. 32(8), 1529–1535 (2009)
Radman, A., Jumari, K., Zainal, N.: Fast and reliable iris segmentation algorithm. IET Image Process. 7(1), 42–49 (2013)
Rapaka, S., Kumar, P.R.: Efficient approach for non-ideal iris segmentation using improved particle swarm optimisation-based multilevel thresholding and geodesic active contours. IET Image Process. 12(10), 1721–1729 (2018)
Sahmoud, S.A., Abuhaiba, I.S.: Efficient iris segmentation method in unconstrained environments. Pattern Recogn. 46(12), 3174–3185 (2013)
Sahmoud, S.A.I.: Enhancing Iris Recognition (2011)
Tan, C.W., Kumar, A.: Accurate iris recognition at a distance using stabilized iris encoding and zernike moments phase features. IEEE Trans. Image Process. 23(9), 3962–3974 (2014)
Wan, H.L., Li, Z.C., Qiao, J.P., Li, B.S.: Non-ideal iris segmentation using anisotropic diffusion. IET Image Process. 7(2), 111–120 (2013)
Wildes, R.P.: Iris recognition: an emerging biometric technology. Proc. IEEE 85(9), 1348–1363 (1997)
Xu, Y., Chuang, T.C., Lai, S.H.: Deep neural networks for accurate iris recognition. In: 2017 4th IAPR Asian Conference on Pattern Recognition (ACPR), pp. 664–669. IEEE (2017)
Yang, Y., Shen, P., Chen, C.: A robust iris segmentation using fully convolutional network with dilated convolutions. In: 2018 IEEE International Symposium on Multimedia (ISM), pp. 9–16. IEEE (2018)
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Sahmoud, S., Fathee, H.N. (2020). Fast Iris Segmentation Algorithm for Visible Wavelength Images Based on Multi-color Space. In: Blanc-Talon, J., Delmas, P., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2020. Lecture Notes in Computer Science(), vol 12002. Springer, Cham. https://doi.org/10.1007/978-3-030-40605-9_21
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DOI: https://doi.org/10.1007/978-3-030-40605-9_21
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