Abstract
Recently, spoof detection has become an important and challenging topic in iris recognition. Based on the textural differences between the counterfeit iris images and the live iris images, we propose an efficient method to tackle this problem. Firstly, the normalized iris image is divided into sub-regions according to the properties of iris textures. Local binary patterns (LBP) are then adopted for texture representation of each sub-region. Finally, Adaboost learning is performed to select the most discriminative LBP features for spoof detection. In particular, a kernel density estimation scheme is proposed to complement the insufficiency of counterfeit iris images during Adaboost training. The comparison experiments indicate that the proposed method outperforms state-of-the-art methods in both accuracy and speed.
Chapter PDF
Similar content being viewed by others
References
Jain, A.K., Ross, A., Prabhaker, S.: Jain, Arun Ross, and Salil Prabhaker. An introduction to biometric recognition. IEEE Trans. on Circuits and Systems for Video Technology 14(1), 4–20 (2004)
Daugman, J.: How iris recognition works. IEEE Trans. On Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Wildes, R.: Iris recognition: An emerging biometric technology. Proceedings of the IEEE 85, 1348–1363 (1997)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 25(12), 1519–1533 (2003)
He, X., An, S., Shi, P.: Statistical texture analysis based approach for fake iris detection using support vector machine. In: Proc. of Int’l Conf. on Biometrics 2007, pp. 540–546 (2007)
Wei, Z., Qiu, X., Sun, Z., Tan, T.: Counterfeit iris detection based on texture analysis. In: Proc. of IEEE Int’l Conf. on Pattern Recognition (ICPR 2008) (2008)
Daugman, J.: Iris recognition and anti-spoof countermeasures. In: Proc. of the 7th Int’l Biometrics Conference (2004)
Lee, E.C., Park, K.R., Kim, J.: Fake iris detection by using purkinje image. In: Zhang, D., Jain, A.K. (eds.) ICB 2005. LNCS, vol. 3832, pp. 397–403. Springer, Heidelberg (2005)
Daugman, J.: Demodulation by complex-valued wavelets for stochastic pattern recognition. Intl. Journal of Wavelets, Multi-resolution and Information Processing 1, 1–17 (2003)
He, Z., Tan, T., Sun, Z., Qiu, X.C.: Boosting ordinal features for accurate and fast iris recognition. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR 2008) (2008)
He, Z., Tan, T., Sun, Z., Qiu, X.C.: Towards accurate and fast iris segmentation for iris biometrics. IEEE Trans. on Pattern Analysis and Machine Intelligence (accepted, 2008)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)
Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)
Sun, Z., Tan, T., Qiu, X.: Graph matching iris image blocks with local binary pattern. In: Proc. of 1st Int’l Conf. on Biometrics, Hong Kong, pp. 366–372 (2006)
Schapire, R.E., Singer, Y.: Improved boosting algorithms using confidence-rated predictions. Machine Learning 37, 297–336 (1999)
Bishop, C.M.: Pattern Recognition and Machine Learning. In: Information Science and Statistics, ch. 6, pp. 291–324. Springer, New York (2006)
Chinese Academy of Sciences Institute of Automation. CASIA Iris Image Database Version 3.0, http://www.cbsr.ia.ac.cn/IrisDatabase.htm
Ice v1.0 iris image database, iris challenge evaluation (ice), http://iris.nist.gov/ice/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
He, Z., Sun, Z., Tan, T., Wei, Z. (2009). Efficient Iris Spoof Detection via Boosted Local Binary Patterns. In: Tistarelli, M., Nixon, M.S. (eds) Advances in Biometrics. ICB 2009. Lecture Notes in Computer Science, vol 5558. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01793-3_109
Download citation
DOI: https://doi.org/10.1007/978-3-642-01793-3_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01792-6
Online ISBN: 978-3-642-01793-3
eBook Packages: Computer ScienceComputer Science (R0)
