Abstract
In this paper, a Gabor filter optimization method based on real-coded genetic algorithm is presented for iris recognition. First, we list Gabor filter parameters and analyzed the validity of the expression for texture features. Then, since Gabor parameters has a great influence in Correct Recognition Rate, we took Gabor kernel parameters as chromosomes and Discriminative Index as fitness to on the CASIA V3 and JLUBR-IRIS for optimization. Moreover, the optimized Gabor filters are adopted to extract features for corresponding iris databases, which can obtain excellent results.
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
Lee, H., Lee, S.H., Kim, T., et al.: Secure user identification for consumer electronics devices. IEEE Transactions on Consumer Electronics 54(4), 1798–1802 (2008)
Daugman, J.: How iris recognition works. IEEE Transactions on Circuits and Systems for Video Technology 14(1), 21–30 (2004)
Kamarainen, J.K., Kyrki, V., Kalviainen, H.: Invariance properties of Gabor filter-based features-overview and applications. IEEE Transactions on Image Processing 15(5), 1088–1099 (2006)
Nabti, M., Bouridane, A.: An effective and fast iris recognition system based on a combined multiscale feature extraction technique. Pattern Recognition 41(3), 868–879 (2008)
Lin, Z., Lu, B.: Iris recognition method based on the optimized Gabor filters. In: 2010 3rd International Congress on Image and Signal Processing (CISP), 4th edn., pp. 1868–1872. IEEE (2010)
Tsai, C.C., Taur, J.S., Tao, C.W.: Iris recognition using gabor filters optimized by the particle swarm technique. In: IEEE International Conference on Systems, Man and Cybernetics SMC, 2008, pp. 921–926. IEEE (2008)
Kumar, A., Hanmandlu, M., Sanghvi, H., et al.: Decision level biometric fusion using Ant Colony Optimization. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3105–3108. IEEE (2010)
Blanco, A., Delgado, M., Pegalajar, M.C.: A real-coded genetic algorithm for training recurrent neural networks. Neural networks 14(1), 93–105 (2001)
Daugman, J.G.: Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. Optical Society of America, Journal, A: Optics and Image Science 2(7), 1160–1169 (1985)
Ma, L., Tan, T., Wang, Y., et al.: Efficient iris recognition by characterizing key local variations. IEEE Transactions on Image Processing 13(6), 739–750 (2004)
Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Machine Learning 3(2), 95–99 (1988)
Houck, C.R., Joines, J.A., Kay, M.G.: A genetic algorithm for function optimization: a Matlab implementation. NCSU-IE TR, 95(09) (1995)
Webster, M.A., De Valois, R.L.: Relationship between spatial-frequency and orientation tuning of striate-cortex cells. Journal of Optical Society of America 7(11), 1124–1132 (1985)
Ma, L., Wang, Y., Tan, T.: Iris recognition based on multichannel gabor filtering. In: The 5th Asian Conference on Computer Vision, pp. 23–25 (2002)
Nabti, M., Bouridane, A.: An effective and fast iris recognition system based on a combined multi-scale feature extraction technique. Pattern Recognition 41(3), 868–879 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer International Publishing Switzerland
About this paper
Cite this paper
He, F., Liu, Y., Zhu, X., Deng, W., Zhang, X., Huo, G. (2013). The Affection of Gabor Parameters to Iris Recognition and Their Optimization. In: Sun, Z., Shan, S., Yang, G., Zhou, J., Wang, Y., Yin, Y. (eds) Biometric Recognition. CCBR 2013. Lecture Notes in Computer Science, vol 8232. Springer, Cham. https://doi.org/10.1007/978-3-319-02961-0_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-02961-0_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02960-3
Online ISBN: 978-3-319-02961-0
eBook Packages: Computer ScienceComputer Science (R0)