Abstract
In order to improve the universality and accuracy of one-to-one iris recognition algorithm, there proposes an iris recognition algorithm based on adaptive optimization Log-Gabor filter and RBF neural network in this paper. Iris amplitude features are extracted with Log-Gabor filter. The selection mutation operator and particle swarm optimization algorithm are used to optimize the filter parameters. Then principal component analysis (PCA) are used to reduce dimensions, thereby reducing the noise and redundancy. Then the Euclidean distance between iris amplitude features are calculated, and the RBF neural network is built for iris recognition. Compared with other iris recognition algorithms on JLU-6.0 iris library and CASIA-Iris-Interval iris library, the recognition rate of this algorithm is higher, and the ROC curve is closer to the coordinate axis, so it has good stability and robustness.
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
References
Wang, W., Zhu, Y., Tan, T.: Identification based on iris recognition. J. Autom. 28(1), 1–10 (2002)
Li, X., Sun, Z., Tan, T.: Overview of iris image quality-assessment. Journal of Image and Graphics 19(6), 813–824 (2014)
Liu, S., et al.: Gabor filtering and adaptive optimization neural network for iris double recognition. In: Zhou, J., et al. (eds.) CCBR 2018. LNCS, vol. 10996, pp. 441–449. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-97909-0_47
Wang, R., Wu, X.: Riemannian manilold image set classification algorithm based on log-gabor wavelet features. Pattern Recogn. Artif. Intell. 30(4), 377–384 (2017)
Gao, S., Zhu, X., Liu, Y., et al.: A quality assessment method of iris image based on support vector machine. J. Fib. Bioeng. Inf. 8(2), 293–300 (2015)
Yuan, C., Sun, X., Wu, Q.J.: Difference co-occurrence matrix using BP neural network for fingerprint liveness detection. Soft. Comput. 13(23), 5157–5169 (2019)
Dua, M., Gupta1, R., Khari, M., Crespo, R.G.: Biometric iris recognition using radial basis function neural network. Soft Comput. 1–23 (2019, in Press)
Si, G.: Research on Capture and Quality Evaluation of Iris Image. Jilin University, Changchun (2016)
Liu, S., Liu, Y., Zhu, X., Feng, J., Lu, S.: Iris location algorithm based on partitioning search. Comput. Eng. Appl. 54(18), 212–217 (2018)
Liu, S., Liu, Y., Zhu, X., Lin, Z., Yang, J.: Ant colony mutation particle swarm optimization for secondary iris recognition. J. Comput. Aided Des. Comput. Graph. 30(8), 1604–1614 (2018)
Zhu, J., Xun, Q., Yi, H., et al.: Virtual network mapping algorithm of node deletion. J. Anhui Univ. (Natural Sciences) 38(5), 37–43 (2014)
Shi, K., et al.: Dynamic barycenter averaging kernel in RBF networks for time series classification. IEEE Access 47(7), 564–576 (2019)
Liu, S., Liu, Y., Zhu, X., Huo, G., Liu, W., Feng, J.: Iris double recognition based on modified evolutionary neural network. J. Electr. Imaging 6(6), 063023 (2017)
Ma, J., Wei, G.: Improved learning algorithm for RBF neural network. Comput. Syst. Appl. 24(2), 84–87 (2013)
CASIA Iris Image Database. http://www.cbsr.ia.ac.cn/english/IrisDatabase.asp
JLU Iris Image Database. http://www.jlucomputer.com/index/irislibrary/irislibrary.html
Chun-yong, Y., Sun, Z.: Parallel implementing improved k-means applied for image retrieval and anomaly delection. J. Multimedia Tools Appl. 76(16), 16911–16927 (2017)
Shao, X., Wang, H., Liu, J., et al.: Sigmoid function based integral-derivative observer and application to autopilot design. J. Mech. Syst. Sig. Process. 84, 113–127 (2017)
Acknowledgments
The authors would like to thank the referee’s advice and acknowledge the support of the National Natural Science Foundation of China (NSFC) under Grant No. 61471181. Jilin Province Industrial Innovation Special Fund Project under Grant No. 2019C053-2. Science and technology project of the Jilin Provincial Education Department under Grant No. JJKH20180448KJ. Thanks also go to the Jilin Provincial Key Laboratory of Biometrics New Technology for supporting this project.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhang, Q. et al. (2019). Iris Recognition Based on Adaptive Optimization Log-Gabor Filter and RBF Neural Network. In: Sun, Z., He, R., Feng, J., Shan, S., Guo, Z. (eds) Biometric Recognition. CCBR 2019. Lecture Notes in Computer Science(), vol 11818. Springer, Cham. https://doi.org/10.1007/978-3-030-31456-9_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-31456-9_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-31455-2
Online ISBN: 978-3-030-31456-9
eBook Packages: Computer ScienceComputer Science (R0)