Abstract
Iris recognition is a prospering biometric method, but some technical difficulties still exist. This paper proposes an iris recognition method based on selected optimal features and statistical learning. To better represent the variation details in irises, we extract features from both spatial and frequency domain. Multi-objective genetic algorithm is then employed to optimize the features. Next step is doing classification of the optimal feature sequence. SVM has recently generated a great interest in the community of machine learning due to its excellent generalization performance in a wide variety of learning problems. We modified traditional SVM as non-symmetrical support vector machine to satisfy the different security requirements in iris recognition applications. Experimental data shows that the selected feature sequence represents the variation details of the iris patterns properly.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Daugman, J.G.: High Confidence Visual Recognition of Persons by a Test of Statistical Independence. IEEE Transactions on Pattern Analysis and Machine Intelligence 15, 1148–1161 (1993)
Ma, L., Wang, Y., Tan, T.: Iris Recognition Based on Multichannel Gabor filtering. In: Proc.5th Asian Conf. Computer Vision, vol. 1, pp. 279–283 (2002)
Simoncelli, E.P.: A Rotation-Invariant Pattern Signature. IEEE International Conference on Image Processing 3, 185–188 (1996)
Gu, H., Pan, H., Wu, F., Zhuang, Y., Pan, Y.: The Research of Iris Recognition Based on Self-similarity. Journal of Computer-Aided Design & Computer Graphics 16, 973–977 (2004)
Simoncelli, E.P., Freeman, W.T.: The Steerable Pyramid: A Flexible Architecture for Multi-Scale Derivative Computation. In: 2nd IEEE International Conference on Image Processing, Washington, DC, vol. 3, pp. 444–447 (1995)
Oliveira, L.S., Sabourin, R.F., Bortolozzi, S.C.Y.: Feature Selection Using Multiobjective Genetic Algorithms for Handwritten Digit Recognition. In: 16th ICPR, pp. 568–571 (2002)
Tan, K.C., Khor, E.F., Lee, T.H.: Multiobjective Evolutionary Algorithms and Applications. Springer, United Kingdom (2004)
DeCoste, D., Scholkopf, B.: Training Invariant Support Vector Machines. Machine Learning 46, 161–190 (2002)
Vapnik, V.N.: Statistical Learning Theory. Wiley, J., New York (1998)
National Laboratory of Pattern Recognition (NLPR), Institute of Automation (IA), Chinese Academy of Sciences (CAS) CASIA Iris Image Database (2003), http://www.sinobiometric.com
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, H., Gao, Z., Wu, F. (2005). Selection of Optimal Features for Iris Recognition. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_14
Download citation
DOI: https://doi.org/10.1007/11427445_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
Online ISBN: 978-3-540-32067-8
eBook Packages: Computer ScienceComputer Science (R0)