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A New Finger Feature Fusion Method Based on Local Gabor Binary Pattern

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Biometric Recognition (CCBR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9967))

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Abstract

This paper proposes a novel multimodal feature fusion method based on local Gabor binary pattern (LGBP). First, the feature maps of three modalities of finger, fingerprint (FP), finger vein (FV) and finger knuckle print (FKP), are respectively extracted using LGBP. The obtained LGBP-coded maps are further explored using local-invariant gray description to generate Local Gabor based Invariant Gray Features (LGIGFs). To reduce pose variations of fingers in imaging, LGIGFs are then weighed by a Gaussian modal. The experimental results show that the proposed method is capable of fusing multimodal feature effectively, and improve correct recognition rate greatly.

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Acknowledgements

This work is jointly supported by National Natural Science Foundation of China (No. 61379102, No. U1343120, No. 61502498).

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Authors and Affiliations

  1. Tianjin Key Lab for Advanced Signal Processing, Civil Aviation University of China, Tianjin, China

    Yihua Shi, Zheng Zhong & Jinfeng Yang

Authors
  1. Yihua Shi
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  2. Zheng Zhong
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  3. Jinfeng Yang
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Corresponding author

Correspondence to Jinfeng Yang .

Editor information

Editors and Affiliations

  1. Sichuan University , Chengdu, China

    Zhisheng You

  2. Tsinghua University , Beijing, China

    Jie Zhou

  3. Beihang University , Beijing, China

    Yunhong Wang

  4. Chinese Academy of Sciences , Beijing, China

    Zhenan Sun

  5. Chinese Academy of Sciences , Beijing, China

    Shiguang Shan

  6. Sun Yat-sen University , Guangzhou, China

    Weishi Zheng

  7. Tsinghua University , Beijing, China

    Jianjiang Feng

  8. Sichuan University , Chengdu, China

    Qijun Zhao

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Shi, Y., Zhong, Z., Yang, J. (2016). A New Finger Feature Fusion Method Based on Local Gabor Binary Pattern. In: You, Z., et al. Biometric Recognition. CCBR 2016. Lecture Notes in Computer Science(), vol 9967. Springer, Cham. https://doi.org/10.1007/978-3-319-46654-5_35

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  • DOI: https://doi.org/10.1007/978-3-319-46654-5_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46653-8

  • Online ISBN: 978-3-319-46654-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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