Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition

  • Shuai Liu
  • Yuanning Liu
  • Xiaodong ZhuEmail author
  • Zhen Liu
  • Guang Huo
  • Tong Ding
  • Kuo Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10996)


The iris image is greatly affected by the collection environment, so, the outputs of different iris categories in the distance recognition algorithm may similar. Neural network recognition algorithm can improve the results distinction, but the same neural network structure has a great difference in the recognition effect of different iris libraries. They all may reduce the accuracy of iris recognition. This paper proposes an iris double recognition algorithm based on Gabor filtering and adaptive optimization neural network. Gabor filtering is used to extract iris features. Hamming distance is used to eliminate most of different categories in the first recognition. The BP neural network that connection weights are optimized by immune particle swarm optimization algorithm is used for the second recognition. The results that the proposed algorithm compares with many algorithms in different iris libraries show that the proposed algorithm can effectively improve iris recognition accuracy.


Iris double recognition Gabor filtering Adaptive optimization neural network Hamming distance Immune particle swarm optimization 



The authors would like to thank the support of the National Natural Science Foundation of China (NSFC) under Grant No. 61471181. Natural Science Foundation of Jilin Province under Grant No. 20140101194JC, 20150101056JC. Science and technology project of the Jilin Provincial Education Department under Grant No. JJKH20180448KJ.


  1. 1.
    Xing-guang, L., Zhe-nan, S., Tie-niu, T.: Overview of iris image quality-assessment. J. J. Image Graph. 19(6), 813–824 (2014)Google Scholar
  2. 2.
    Liu, S., Liu, Y., Zhu, X., Huo, G., Cui, J., Chen, Y.: Iris recognition based on adaptive gabor filter. In: Zhou, J., Wang, Y., Sun, Z., Xu, Y., Shen, L., Feng, J., Shan, S., Qiao, Yu., Guo, Z., Yu, S. (eds.) CCBR 2017. LNCS, vol. 10568, pp. 383–390. Springer, Cham (2017). Scholar
  3. 3.
    Shuai, L., Yuan-ning, L., Xiao-dong, Z., et al.: Iris double recognition based on modified evolutionary neural network. J. Electron. Imaging 26(6), 063023 (2017)Google Scholar
  4. 4.
    Fei, H., Yuan-ning, L., Xiao-dong, Z., et al.: Score level fusion scheme based on adaptive local Gabor features for face-iris-fingerprint multimodal biometric. J. Electron. Imaging 23(3), 033019 (2014)CrossRefGoogle Scholar
  5. 5.
    Zhi-ming, L.: Research on iris liveness detection algorithm based on convolutional neural network. J. Comput. Eng. 42(5), 239–243 (2016)Google Scholar
  6. 6.
    Abdelkawy, M.A., Bhrawy, A.H., Zerrad, E., et al.: Application of tanh method to complex coupled nonlinear evolution equation. J Acta Phys. Pol. A 129(3), 278–283 (2016)CrossRefGoogle Scholar
  7. 7.
    Sheng, L., Qian, S.Q., Ye, Y.Q.: An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters. J. Eng. Optim. 49(9), 1463–1482 (2017)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Qi, B., Kairui, Z., Xinmin, W., et al.: System identification method for small unmanned helicopter based on improved particle swarm optimization. J. Bionic Eng. 13(3), 504–514 (2016)CrossRefGoogle Scholar
  9. 9.
    Liang, X., Huang, M., Ning, T.: Flexible job shop scheduling based on improved hybrid immune algorithm. J. Ambient Intell. Hum. Comput. 9(1), 165–171 (2018)CrossRefGoogle Scholar
  10. 10.
    Dongfeng, W., Li, M., Wenjie, Z.: Improved bare bones particle swarm optimization with adaptive search center. J. Chin. J. Comput. 39(12), 2652–2667 (2016)MathSciNetGoogle Scholar
  11. 11.
    Guang, H., Yuan-ning, L., Xiao-dong, Z., et al.: Face-iris multimodal biometric scheme based on feature level fusion. J. Electron. Imaging 24(6), 063020 (2015)CrossRefGoogle Scholar
  12. 12.
  13. 13.
    JLU Iris Image Database.
  14. 14.
    Xiuyan, L., Qian, H., Jianming, W., et al.: ERT image reconstruction based on improved CG method. Chin. J. Sci. Instrum. 37(7), 1673–1679 (2016)Google Scholar
  15. 15.
    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)CrossRefGoogle Scholar
  16. 16.
    Huo, G., Liu, Y., Zhu, X., et al.: Secondary iris recognition method based on local energy- orientation feature. J. Electron. Imaging 24(1), 013033 (2015)CrossRefGoogle Scholar
  17. 17.
    Tan, C.W., Kumar, A.: Accurate iris recognition at a distance using stabilized iris encoding and zernike moments phase features. J. IEEE Trans. Image Process. 23(9), 3962–3974 (2014). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar
  18. 18.
    Nalla, P.R., Kumar, A.: Toward more accurate iris recognition using cross-spectral matching. J. IEEE Trans. Image Process. 26(1), 208–221 (2016). A Publication of the IEEE Signal Processing SocietyMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Shuai Liu
    • 1
    • 2
  • Yuanning Liu
    • 1
    • 3
  • Xiaodong Zhu
    • 1
    • 3
  • Zhen Liu
    • 3
    • 4
  • Guang Huo
    • 5
  • Tong Ding
    • 1
    • 2
  • Kuo Zhang
    • 1
    • 3
  1. 1.Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of EducationJilin UniversityChangchunChina
  2. 2.College of SoftwareJilin UniversityChangchunChina
  3. 3.College of Computer Science and TechnologyJilin UniversityChangchunChina
  4. 4.Graduate School of EngineeringNagasaki Institute of Applied ScienceNagasakiJapan
  5. 5.College of Information EngineeringNortheast Electric Power UniversityJilinChina

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