Abstract
Aiming at the problem of multi-category iris recognition, there proposes a method of iris recognition algorithm based on adaptive Gabor filter. Use DE-PSO to adaptive optimize the Gabor filter parameters. DE-PSO is composed of particle swarm optimization and differential evolution algorithm. Use 16 groups of 2D-Gabor filters with different frequencies and directions to process iris images. According to the direction and frequency of maximum response amplitude, transform iris features into 512-bit binary feature encoding. Calculate the Hamming distance of feature code and compare with the classification threshold, determine iris the type of iris. Experiment on a variety of iris databases with multiple Gabor filter algorithms, the results showed that this algorithm has higher recognition rate, the ROC curve is closer to the coordinate axis and the robustness is better, compare with other Gabor filter algorithm.
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References
Huo, G., et al.: 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. LNCS, vol. 9428, pp. 341–348. Springer, Cham (2015). doi:10.1007/978-3-319-25417-3_40
Zhu, L., Yuan, W.: An eyelash extraction method based on improved ant colony algorithm. J. Opto-Electron. Eng. 43(6), 44–50 (2016)
Fei, H., Ye, H., Han, W., et al.: Deep learning architecture for iris recognition based on optimal Gabor filters and deep belief network. J. Electron. Imaging 26(2), 023005 (2017)
Zhou, J., Ji, Z., Shen, L., et al.: PSO based memetic algorithm for face recognition Gabor filters selection. In: IEEE Workshop on Memetic Computing, Paris, France. IEEE Computer Society (2011)
Song, L., Li-jun, L., Man, Z.: Prediction for short-term traffic flow based on modified PSO optimized BP neural network. Syst. Eng.-Theory Pract. 32(9), 2045–2049 (2012)
Wang, D.-F., Meng, L., Zhao, W.-J.: Improved bare bones particle swarm optimization with adaptive search center. Chin. J. Comput. 39(12), 2652–2666 (2016)
Lu, C.: An iris recognition system based on feature fusion and optimized extreme learning machine algorithm. Comput. Appl. Softw. 33(7), 326–333 (2016)
Gao, S., Zhu, X., Liu, Y., et al.: A quality assessment method of iris image based on support vector machine. J. Fiber Bioeng. Inform. 8(2), 293–300 (2015)
Huan-li, L., Li-hong, G., Xiao-ming, L., et al.: Iris recognition based on SCCS-LBP. Opt. Precis. Eng. 21(8), 2129–2136 (2013)
Daugman, J.G.: How iris recognition works. IEEE Trans. Circuits Syst. Video Technol. 14(1), 21–30 (2004)
Bi, X., Pan, T.: An image enhancement method based on improved teaching-learning-based optimization algorithm. J. Harbin Eng. Univ. 37(12), 1716–1721 (2016)
Li, H., Guo, L., Wang, X., et al.: Iris recognition based on weighted Gabor filter. J. Jilin Univ. (Eng. Technol. Edn.) 44(1), 196–202 (2014)
Carlisle, A., Dozier, G.: An off-the-shelf PSO. In: Proceedings of Workshop on Particle Swarm Optimization, pp. 1–6 (2001)
JLU Iris Image Database. http://biis.jlu.edu.cn
CASIA Iris Image Database. http://www.cbsr.ia.ac.cn/IrisDatabase.htm
Zhao, T.: Research on iris feature extraction. School of Computer Science and Technology, Jilin University, Changchun, China (2016)
Yu, Z., Lu, Y., Zhang, J., et al.: Progressive semisupervised learning of multiple classifiers. IEEE Trans. Cybern. 99, 1–14 (2017)
Shaikh, N.F., Doye, D.D.: An adaptive central force optimization (ACFO) and feed forward back propagation neural network (FFBNN) based iris recognition system. J. Intell. Fuzzy Syst. 30(4), 2083–2094 (2016)
Olanrewaju, O.A., Mbohwa, C.: Evaluating factors responsible for energy consumption: connection weight approach. In: IEEE Electrical Power and Energy Conference, Canada (2016)
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, Natural Science Foundation of Jilin Province under Grant Nos. 20140101194JC, 20150101056JC.
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Liu, S., Liu, Y., Zhu, X., Huo, G., Cui, J., Chen, Y. (2017). Iris Recognition Based on Adaptive Gabor Filter. In: Zhou, J., et al. Biometric Recognition. CCBR 2017. Lecture Notes in Computer Science(), vol 10568. Springer, Cham. https://doi.org/10.1007/978-3-319-69923-3_41
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DOI: https://doi.org/10.1007/978-3-319-69923-3_41
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