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Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition

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Book cover Biometric Recognition (CCBR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10996))

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Abstract

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.

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Acknowledgments

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.

Author information

Authors and Affiliations

  1. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, 130012, China

    Shuai Liu, Yuanning Liu, Xiaodong Zhu, Tong Ding & Kuo Zhang

  2. College of Software, Jilin University, Changchun, 130012, China

    Shuai Liu & Tong Ding

  3. College of Computer Science and Technology, Jilin University, Changchun, 130012, China

    Yuanning Liu, Xiaodong Zhu, Zhen Liu & Kuo Zhang

  4. Graduate School of Engineering, Nagasaki Institute of Applied Science, Nagasaki, 851-0193, Japan

    Zhen Liu

  5. College of Information Engineering, Northeast Electric Power University, Jilin, 132012, China

    Guang Huo

Authors
  1. Shuai Liu
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  2. Yuanning Liu
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  3. Xiaodong Zhu
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  4. Zhen Liu
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  5. Guang Huo
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  6. Tong Ding
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  7. Kuo Zhang
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Corresponding author

Correspondence to Xiaodong Zhu .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Jie Zhou

  2. Beihang University, Beijing, China

    Yunhong Wang

  3. Chinese Academy of Sciences, Beijing, China

    Zhenan Sun

  4. Xinjiang University, Urumqi, China

    Zhenhong Jia

  5. Tsinghua University, Beijing, China

    Jianjiang Feng

  6. Chinese Academy of Sciences, Beijing, China

    Shiguang Shan

  7. Xinjiang University, Urumqi, China

    Kurban Ubul

  8. Tsinghua University, Shenzhen, China

    Zhenhua Guo

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Liu, S. et al. (2018). Gabor Filtering and Adaptive Optimization Neural Network for Iris Double Recognition. In: Zhou, J., et al. Biometric Recognition. CCBR 2018. Lecture Notes in Computer Science(), vol 10996. Springer, Cham. https://doi.org/10.1007/978-3-319-97909-0_47

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  • DOI: https://doi.org/10.1007/978-3-319-97909-0_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97908-3

  • Online ISBN: 978-3-319-97909-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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