Abstract
Segmenting iris from images captured under the visible wavelength spectrum represents a challenging task. To address this problem, a new iris segmentation method based on the Circular Gabor Filter (CGF) and optimization is proposed. The CGF is utilized to localize the rough position of the pupil center. According to the localization result, few pixels of the pupil (iris) and non-pupil (non-iris) regions are set as initial pixels in the related region. Next, by optimization scheme, initial pixels are automatically propagated over the image until they cover the whole pupil (iris) and non-pupil (non-iris) regions. The originality of the proposed method includes developing a new method to localize the iris and eyelids boundaries simultaneously, and furthermore robust to reflections. Experimental results on the non-cooperative UBIRIS.v1 iris images reveal the effectiveness of the proposed method in comparison with some of state-of-the-art methods. The results include classification error rates of 2.08 and 3.05 % for session one and two of the UBIRIS.v1 database, respectively.
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References
Wildes, R.P.: Iris recognition: an emerging biometrie technology. In: Proceedings of the IEEE 85(8), 1348–1363 (1997)
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)
Ma, L.; Tan, T.; Wang, Y.; Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003)
Sung, E.; Chen, X.; Zhu, J.; Yang, J.: Towards non-cooperative iris recognition systems. In: the 7th International Conference on Control, Automation, Robotics and Vision, Singapore 2002, pp. 990–995
Proença, H.; Alexandre, L.A.: Iris segmentation methodology for non-cooperative recognition. IEE Proc. Vis. Image Signal Process. 153(2), 199–205 (2006)
Proença, H.; Alexandre, L.A.: UBIRIS: a noisy iris image database. In: 13th International Conference on Image Analysis and Processing. Lecture Notes in Computer Science, vol. 3617, pp. 970–977. Springer, Berlin (2005)
Puhan, N.B.; Sudha, N.; Sivaraman Kaushalram, A.: Efficient segmentation technique for noisy frontal view iris images using Fourier spectral density. Signal Image Video Process. 5(1), 105–119 (2011)
Jeong, D.S.; Hwang, J.W.; Kang, B.J.; Park, K.R.; Won, C.S.; Park, D.K.; Kim, J.: A new iris segmentation method for non-ideal iris images. Image Vis. Comput. 28(2), 254–260 (2010)
Tan, T.; He, Z.; Sun, Z.: Efficient and robust segmentation of noisy iris images for non-cooperative iris recognition. Image Vis. Comput. 28(2), 223–230 (2010)
Schuckers, S.A.C.; Schmid, N.A.; Abhyankar, A.; Dorairaj, V.; Boyce, C.K.; Hornak, L.A.: On techniques for angle compensation in nonideal iris recognition. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(4), 1176–1190 (2007)
Daugman, J.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(4), 1167–1175 (2007)
Shah, S.; Ross, A.: Iris segmentation using geodesic active contours. IEEE Trans. Inf. Forensics Secur. 4(4), 824–836 (2009)
Liu, X.: Optimizations in iris recognition. 3406882, University of Notre Dame (2007)
Arvacheh, M.E.: A study of segmentation and normalization for iris recognition systems. MR23443, University of Waterloo (Canada) (2006)
Roy, K.; Bhattacharya, P.; Suen, C.Y.: Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs. Eng. Appl. Artif. Intell. 24(3), 458–475 (2011)
Ibrahim, M.T.; Khan, T.M.; Khan, S.A.; Khan, M.A.; Guan, L.: Iris localization using local histogram and other image statistics. Opt. Lasers Eng. 50(4), 645–654 (2012). doi:10.1016/j.optlaseng.2011.11.008
Li, P.H.; Ma, H.W.: Iris recognition in non-ideal imaging conditions. Pattern Recognit. Lett. 33(7), 1012–1018 (2012). doi:10.1016/j.patrec.2011.06.017
Al-Zubi, R.; Abu-Al-Nadi, D.: Automated personal identification system based on human iris analysis. Pattern Anal. Appl. 10(2), 147–164 (2007). doi:10.1007/s10044-006-0058-2
Boles, W.W.; Boashash, B.: A human identification technique using images of the iris and wavelet transform. IEEE Trans. Signal Process. 46(4), 1185–1188 (1998)
Chen, Y.; Adjouadi, M.; Han, C.; Wang, J.; Barreto, A.; Rishe, N.; Andrian, J.: A highly accurate and computationally efficient approach for unconstrained iris segmentation. Image Vis. Comput. 28(2), 261–269 (2010)
Donida Labati, R.; Scotti, F.: Noisy iris segmentation with boundary regularization and reflections removal. Image and Vis. Comput. 28(2), 270–277 (2010)
He, Z.; Tan, T.; Sun, Z.; Qiu, X.: Toward accurate and fast iris segmentation for iris biometrics. IEEE Trans. Pattern Anal. Mach. Intell. 31(8), 1670–1684 (2009)
Li, P.; Liu, X.; Xiao, L.; Song, Q.: Robust and accurate iris segmentation in very noisy iris images. Image Vis. Comput. 28(2), 246–253 (2010)
Ling, L.L.; de Brito, D.F.: Fast and efficient iris image segmentation. J. Med. Biol. Eng. 30(5), 381–392 (2010)
Ma, L.; Tan, T.; Wang, Y.; Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13(5), 739–750 (2004)
Roy, K.; Bhattacharya, P.; Suen, C.Y.: Iris segmentation using game theory. Signal Image Video Process. 1–15 (2010)
Roy, K.; Bhattacharya, P.; Suen, C.: Iris recognition using shape-guided approach and game theory. Pattern Analy. Appl. 14(4), 329–348 (2011). doi:10.1007/s10044-011-0229-7
Fitzgibbon, A.; Pilu, M.; Fisher, R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21(4), 476–480 (1999)
Daugman, J.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. J. Opt. Soc. Am. 2(6), 1160–1169 (1985). doi:10.1364/JOSAA.2.001160
Gabor, D.: Theory of communication. J. Inst. Electr. Eng. 93(26), 429–441 (1946)
Du, Y.; Belcher, C.; Zhou, Z.: Scale invariant Gabor descriptor-based noncooperative iris recognition. Eurasip J. Adv. Signal Process. (2010). doi:10.1155/2010/936512
Yang, J.; Liu, L.; Jiang, T.; Fan, Y.: A modified Gabor filter design method for fingerprint image enhancement. Pattern Recog. Lett. 24(12), 1805–1817 (2003)
Yang, P.; Du, B.; Shan, S.; Gao, W.: A novel pupil localization method based on gaboreye model and radial symmetry operator. In: International Conference on Image Processing, pp. 67–70 (2004)
Yanfang, Z.; Nongliang, S.; Yang, G.; Maoyong, C.: A new eye location method based on Ring Gabor Filter. In: IEEE International Conference on Automation and Logistics, 1–3 Sept. 2008, pp. 301–305 (2008)
Zhang, J.; Tan, T.; Ma, L.: Invariant texture segmentation via circular gabor filters. In: International Conference on Pattern Recognition, pp. 901–904 (2002)
Jain, A.K.; Farrokhnia, F.: Unsupervised texture segmentation using Gabor filters. Pattern Recognit. 24(12), 1167–1186 (1991). doi:10.1016/0031-3203(91)90143-s
De Valois, R.L.; Albrecht, D.G.; Thorell, L.G.: Spatial frequency selectivity of cells in macaque visual cortex. Vis. Res. 22(4), 545–559 (1982). doi:10.1016/0042-6989(82)90113-4
Smallman, H.S.; MacLeod, D.I.; He, S.; Kentridge, R.: Fine grain of the neural representation of human spatial vision. J. Neurosci. 16(4), 1852–1859 (1996)
Mathews, J.H.; Fink, K.D.: Numerical Methods Using MATLAB, 3rd edn. Prentice Hall College Div, New Jersey (1999)
Levin, A.; Lischinski, D.; Weiss, Y.: Colorization usingoptimization. In: Annual Symposium of the ACM SIGGRAPH, Grenoble, France, pp. 689–694 (2004)
Grady, L.: Random walks for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006)
Weiss, Y.: Segmentation using eigenvectors: a unifying view. In: 7th IEEE International Conference on Computer Vision, Greece, pp. 975–982 (1999)
Proença, H.; Alexandre, L.A.: The NICE.I: Noisy Iris Challenge Evaluation—Part I. In: 1st IEEE International Conference on Biometrics: Theory, Applications, and Systems, Crystal City (2007)
Daugman, J.: High confidence recognition of persons by iris patterns. In: 2001 IEEE 35th International Carnahan Conference on Security Technology, Oct 2001, pp. 254–263 (2001)
Libor Masek, P.K.: MATLAB source code for a biometric identification system based on iris patterns. The School of Computer Science and Software Engineering, The University of Western Australia (2003)
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Radman, A., Jumari, K. & Zainal, N. Iris Segmentation in Visible Wavelength Images using Circular Gabor Filters and Optimization. Arab J Sci Eng 39, 3039–3049 (2014). https://doi.org/10.1007/s13369-013-0924-3
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DOI: https://doi.org/10.1007/s13369-013-0924-3