Abstract
Iris recognition is a kind of important biometrics technology for personal identify verification, iris classification method has been achieved more attention according to different feature extraction. Binary feature extraction is one of the most effective techniques employed for the human iris recognition problem. However, the selection of a particular set of features is often problematic, so iris classifier performance isn’t satisfied. Based on AdaBoost, an enhanced algorithm for iris classifier is presented in this paper. The algorithm will achieve a stronger iris classifier (iris feature template) by lifting weaker similarity classifiers based on AdaBoost through training samples. Simulation results on CASIA iris database show that the method is effective.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Wang, H.C., Zhang, L.M.: A New Adaboost Fast Training Algorithm. Journal of Fudan University 43, 27–33 (2004)
Shen, L.L., Bai, L.: Gabor Feature Selection For Face Recognition Using Improved AdaBoost Learning. In: Li, S.Z., Sun, Z., Tan, T., Pankanti, S., Chollet, G., Zhang, D. (eds.) IWBRS 2005. LNCS, vol. 3781, pp. 39–49. Springer, Heidelberg (2005)
Sun, Z.N., Tan, T.N., Wang Y.H.: Robust Encoding of Local Ordinal Measures: A General Framework of Iris Recognition. In: European Conference on Computer Vision workshop on Biometric Authentication, pp. 270–282 (2004)
Vatsa, M., Singh. R., Gupta, P.: Comparison of Iris Recognition Algorithms. In: IEEE International Conference on Intelligent Sensing and Information Processing. Chennai, India, pp. 354–358 (2004)
John, D.: High Confidence Visual Recognition of Persons by A Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)
Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Transactions on Signal Processing 46, 1185–1188 (1998)
Bae, K., Noh, S., Kim, J.: Iris Feature Extraction Using Independent Component Analysis. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 838–844. Springer, Heidelberg (2003)
Raul, S., Carmen, S.: Iris Recognition with Low Template Size. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 324–329. Springer, Heidelberg (2001)
Kong, W.K., Zhang, D.: Detecting Eyelash and Reflection for Accurate Iris Segmentation. International Journal of Pattern Recognition and Artificial Intelligence 17, 1025–1034 (2003)
Ma, L., Tan, T., Wang, Y.: Efficient Iris Recognition by Characterizing Key Local Variations. IEEE Transactions on Image Processing 13, 739–750 (2004)
Daouk, C.H., El-Esber, L.A., Kammoun, F.D.: Iris Recognition. In: Proceedings of 2nd IEEE International Symposium on Signal Processing and Information Technology, pp. 558–562. IEEE Computer Society Press, Los Alamitos (2002)
Tian, Q.C., Pan, Q., Liang, Y.: Fast Iris Boundary Localization Algorithm Supervised by Pupil Center. Journal of System Simulation 18, 1777–1780 (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tian, QC., Zhao, XL., Wu, XJ., Li, LS., Liu, L. (2007). Iris Classifier Enhanced Algorithm Based on AdaBoost. In: Huang, DS., Heutte, L., Loog, M. (eds) Advanced Intelligent Computing Theories and Applications. With Aspects of Contemporary Intelligent Computing Techniques. ICIC 2007. Communications in Computer and Information Science, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74282-1_112
Download citation
DOI: https://doi.org/10.1007/978-3-540-74282-1_112
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74281-4
Online ISBN: 978-3-540-74282-1
eBook Packages: Computer ScienceComputer Science (R0)