This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
Toward Accurate and Fast Iris Segmentation for Iris Biometrics
September 2009 (vol. 31 no. 9)
pp. 1670-1684
Zhaofeng He, Institute of Automation, Chinese Academy of Sciences, Beijing
Tieniu Tan, Institute of Automation, Chinese Academy of Sciences, Beijing
Zhenan Sun, Institute of Automation, Chinese Academy of Sciences, Beijing
Xianchao Qiu, Institute of Automation, Chinese Academy of Sciences, Beijing
Iris segmentation is an essential module in iris recognition because it defines the effective image region used for subsequent processing such as feature extraction. Traditional iris segmentation methods often involve an exhaustive search of a large parameter space, which is time consuming and sensitive to noise. To address these problems, this paper presents a novel algorithm for accurate and fast iris segmentation. After efficient reflection removal, an Adaboost-cascade iris detector is first built to extract a rough position of the iris center. Edge points of iris boundaries are then detected, and an elastic model named pulling and pushing is established. Under this model, the center and radius of the circular iris boundaries are iteratively refined in a way driven by the restoring forces of Hooke's law. Furthermore, a smoothing spline-based edge fitting scheme is presented to deal with noncircular iris boundaries. After that, eyelids are localized via edge detection followed by curve fitting. The novelty here is the adoption of a rank filter for noise elimination and a histogram filter for tackling the shape irregularity of eyelids. Finally, eyelashes and shadows are detected via a learned prediction model. This model provides an adaptive threshold for eyelash and shadow detection by analyzing the intensity distributions of different iris regions. Experimental results on three challenging iris image databases demonstrate that the proposed algorithm outperforms state-of-the-art methods in both accuracy and speed.

[1] A.K. Jain, A. Ross, and S. Prabhaker, “An Introduction to Biometric Recognition,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 4-20, 2004.
[2] J. Daugman, “Statistical Richness of Visual Phase Information: Update on Recognition Persons by Iris Patterns,” Int'l J. Computer Vision, vol. 45, no. 1, pp. 25-38, 2001.
[3] J. Daugman, “How Iris Recognition Works,” IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, Jan. 2004.
[4] J. Daugman, “New Methods in Iris Recognition,” IEEE Trans. System, Man, and Cybernetics—Part B: Cybernetics, vol. 37, no. 5, pp.1167-1175, 2007.
[5] W. Boles and B. Boashah, “A Human Identification Technique Using Images of the Iris and Wavelet Transform,” IEEE Trans. Signal Processing, vol. 46, pp. 1185-1188, 1998.
[6] Z. Sun, T. Tan, and Y. Wang, “Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition,” Proc. European Conf. Computer Vision Int'l Workshop Biometric Authentication, pp. 270-282, 2004.
[7] L. Ma, T. Tan, Y. Wang, and D. Zhang, “Efficient Iris Recognition by Characterizing Key Local Variations,” IEEE Trans. Image Processing, vol. 13, pp. 739-750, 2004.
[8] R. Wildes, “Iris Recognition: An Emerging Biometric Technology,” Proc. IEEE, vol. 85, no. 9, pp. 1348-1365, 1997.
[9] K.W. Bowyer, K. Hollingsworth, and P.J. Flynn, “Image Understanding for Iris Biometrics: A Survey,” Computer Vision and Image Understanding, vol. 110, no. 2, pp. 281-307, 2008.
[10] 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, 2007.
[11] T.A. Camus and R. Wildes, “Reliable and Fast Eye Finding in Close-Up Images,” Proc. 16th Int'l Conf. Pattern Recognition, vol. 2, pp. 414-417, 2002.
[12] C. Tisse, L. Martin, L. Torres, and M. Robert, “Person Identification Technique Using Human Iris Recognition,” Proc. 15th Int'l Conf. Vision Interface, pp. 294-299, 2002.
[13] X.M. Liu, K.W. Bowyer, and P.J. Flynn, “Experiments with an Improved Iris Segmentation Algorithm,” Proc. Fourth IEEE Workshop Automatic Identification Advanced Technologies, 2005.
[14] X. Feng, C. Fang, X. Ding, and Y. Wu, “Iris Localization with Dual Coarse-to-Fine Strategy,” Proc. 18th Int'l Conf. Pattern Recognition, vol. 4, pp. 553-556, 2006.
[15] D.H. Cho, K.R. Park, and D.W. Rhee, “Real-Time Iris Localization for Iris Recognition in Cellular Phone,” Proc. Sixth Int'l Conf. Software Eng., Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS Int'l Workshop Self-Assembling Wireless Networks, pp. 254-259, 2005.
[16] E. Trucco and M. Razeto, “Robust Iris Location in Close-Up Images of the Eye,” Pattern Analysis & Applications, vol. 8, no. 3, pp. 247-255, 2005.
[17] W.K. Kong and D. Zhang, “Accurate Iris Segmentation Based on Novel Reflection and Eyelash Detection Model,” Proc. Int'l Symp. Intelligent Multimedia, Video and Speech Processing, pp. 263-266, 2001.
[18] J. Huang, Y. Wang, T. Tan, and J. Cui, “A New Iris Segmentation Method for Recognition,” Proc. 17th Int'l Conf. Pattern Recognition, pp. 554-557, 2004.
[19] Z. He, T. Tan, Z. Sun, and X. Qiu, “Robust Eyelid, Eyelash and Shadow Localization for Iris Recognition,” Proc. Int'l Conf. Image Processing, 2008.
[20] A.K. Bachoo and J.R. Tapamo, “Texture Detection for Segmentation of Iris Images,” Proc. Ann. Research Conf. South African Inst. for Computer Scientist and Information Technologists, pp. 236-243, 2005.
[21] H. Proença and L.A. Alexandre, “Iris Segmentation Methodology for Non-Cooperative Recognition,” IEE Proc.—Vision, Image and Signal Processing, vol. 153, pp. 199-205, 2006.
[22] E.M. Arvacheh and H.R. Tizhoosh, “Iris Segmentation: Detecting Pupil, Limbus and Eyelids,” Proc. Int'l Conf. Image Processing, pp.2453-2456, 2006.
[23] A. Abhyankar and S. Schuckers, “Active Shape Models for Effective Iris Segmentation,” Proc. SPIE—Biometric Technology for Human Identification, vol. 6202, 2006.
[24] V. Dorairaj, N.A. Schmid, and G. Fahmy, “Performance Evaluation of Non-Ideal Iris Based Recognition System Implementing Global ICA Encoding,” Proc. Int'l Conf. Image Processing, pp. 285-288, 2005.
[25] S. Rakshit and D.M. Monro, “Pupil Shape Description Using Fourier Series,” Proc. IEEE Workshop Signal Processing Applications for Public Security and Forensics, pp. 1-4, 2007.
[26] X. Li, “Modeling Intra-Class Variation for Non-Ideal Iris Recognition,” Proc. Int'l Conf. Biometrics, pp. 419-427, 2006.
[27] Z. He, Z. Sun, T. Tan, X. Qiu, C. Zhong, and W. Dong, “Boosting Ordinal Features for Accurate and Fast Iris Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2008.
[28] J. Cui, L. Ma, T. Tan, X. Hou, and Y. Wang, “An Iris Detection Method Based on Structure Information,” Proc. Int'l Workshop Biometric Recognition Systems, pp. 157-164, 2005.
[29] P. Viola and M.J. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, pp. 511-518, 2001.
[30] R.E. Schapire and Y. Singer, “Improved Boosting Algorithms Using Confidence-Rated Predictions,” Machine Learning, vol. 37, pp. 297-336, 1999.
[31] Z. He, T. Tan, and Z. Sun, “Iris Localization via Pulling and Pushing,” Proc. 18th Int'l Conf. Pattern Recognition, vol. 4, pp. 366-369, 2006.
[32] H.D. Young and R.A. Freedman, University Physics with Modern Physics. Addison-Wesley, 2003.
[33] S. Haykin, Neural Networks: A Comprehensive Foundation. Prentice Hall, 1999.
[34] C.H. Reinsch, “Smoothing by Spline Functions,” Numerische Mathematik, vol. 10, pp. 177-183, 1967.
[35] G. Wahba, “Smoothing Noisy Data with Spline Functions,” Numerische Mathematik, vol. 24, pp. 383-393, 1975.
[36] R.C. Gonzales and P. Wintz, Digital Image Processing. Addison Wesley Longman, 1987.
[37] H. Proença and L.A. Alexandre, “UBIRIS: A Noisy Iris Image Database,” Lecture Notes in Computer Science, vol. 3617, pp. 970-977, http:/iris.di.ubi.pt, 2005.
[38] CASIA Iris Image Database V-3.0, http://www.cbsr.ia.ac.cniris-database.htm , 2008.
[39] Iris Challenge Evaluation (ICE), http://iris.nist.govICE/, 2009.

Index Terms:
Biometrics, iris segmentation, reflection removal, eyelid localization, eyelash and shadow detection, edge fitting.
Citation:
Zhaofeng He, Tieniu Tan, Zhenan Sun, Xianchao Qiu, "Toward Accurate and Fast Iris Segmentation for Iris Biometrics," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 9, pp. 1670-1684, Sept. 2009, doi:10.1109/TPAMI.2008.183
Usage of this product signifies your acceptance of the Terms of Use.