Abstract
Different annulus regions of iris texture have various distribution characteristics. All the previous feature extraction methods are unable to make a difference between relevance of intra-annulus feature and difference of inter-annulus feature. With an analysis of relevance of intra-annulus, this paper proposes a kind of feature extraction method based on texture regions. The method firstly uses 2D-Gabor filter to independently extract and encode texture features from different regions respectively, and then the set of feature vectors are applied to classification and recognition by SVM classifier. The experimental results show that the proposed method has quite high recognition accuracy.
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
Daugman, J.G.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)
Daugman, J.G.: New methods in iris recognition. IEEE Trans. Syst. Man Cybern. B 37(5), 1167–1175 (2007)
Vatsa, M., Singh, R., Noore, A.: Improving iris recognition performance using segmentation, quality enhancement, match score fusion, and indexing. IEEE Trans. Syst. Man Cybern. B 38(4), 1021–1035 (2008)
Ma, L., Tan, T., Wang, Y., Zhang, D.: Personal identification based on iris texture analysis. IEEE Trans. Pattern Anal. Mach. Intell. 25(12), 1519–1533 (2003)
Ding, S.F., Qi, B.J., Tan, H.Y.: An overview on theory and algorithm of support vector machines. J. Univ. Electron. Sci. Technol. China 40(1), 2–10 (2011)
CASIA V4-Interval Iris Image Database. http://www.cbsr.ia.ac.cn/irisdatabase.htm
He, F., Liu, Y., Zhu, X.: Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric. J. Electron. Imag. 23(3), 572–579 (2014)
Boles, W.W., Boashash, B.: A Human Identification Technique Using Images of the Iris and Wavelet Transform. IEEE Trans. Signal Processing 46(4), 1185–1188 (1998)
Wildes, R.P., et al.: A machine-vision system for iris recognition. In: Machine Vision and Applications. Springer, Heidelberg (1996)
Yao, P., Ye, X.Y., Zhuang, Z.: An iris recognition algorithm combining local frequency features with local orientation features. Acta Electron. Sinica 35(4), 663–667 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Huo, G. et al. (2015). An Iris Recognition Method Based on Annule-energy Feature. In: Yang, J., Yang, J., Sun, Z., Shan, S., Zheng, W., Feng, J. (eds) Biometric Recognition. CCBR 2015. Lecture Notes in Computer Science(), vol 9428. Springer, Cham. https://doi.org/10.1007/978-3-319-25417-3_40
Download citation
DOI: https://doi.org/10.1007/978-3-319-25417-3_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25416-6
Online ISBN: 978-3-319-25417-3
eBook Packages: Computer ScienceComputer Science (R0)