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Shuffle Dense Networks for Biometric Authentication

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Cyberspace Safety and Security (CSS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 11982))

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Abstract

In recent years, with the continuous development of artificial intelligence technology, biometric authentication based on computer vision technology have also been developed rapidly. In this paper, we propose a novel Shuffle dense networks (SDN) with combining ShuffleNet and DenseNet for biometric authentication. ShuffleNet is an extremely computation-efficient structure that can obtain more channels information. DenseNet makes full use of all hierarchical features, which can facilitate the flow of information. Specifically, dense skip connections is adopted for combining the low-level features and the high-level features to enhance the performance of the reconstruction and residual learning is applied for easing the difficulty of training the deep neural network. In addition, the grouped convolutions are introduced for reducing computational complexity and the number of parameters. What’s more, the shuffle dense connections are proposed for mitigating the grouped convolutions problem of lacking information exchange between the groups. The proposed method is evaluated quantitatively and qualitatively on four benchmark datasets, and the experimental results of super-resolution illustrated that our SDN achieves great performance over the state-of-the-art frameworks.

Supported by organization National Nature Science Foundation of China Grand No: 61371156.

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Acknowledgements

This research supported by National Nature Science Foundation of China Grand No: 61371156. The authors would like to thank the anonymous reviews for their helpful and constructive comments and suggestions regarding this manuscript.

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Authors and Affiliations

  1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, Anhui, China

    Huaijuan Zang, Qiuyu Li, Ailian Chen, Jinlong Chen & Shu Zhan

Authors
  1. Huaijuan Zang
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  2. Qiuyu Li
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  3. Ailian Chen
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  4. Jinlong Chen
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  5. Shu Zhan
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Corresponding author

Correspondence to Shu Zhan .

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Editors and Affiliations

  1. Rutgers University, Newark, NJ, USA

    Jaideep Vaidya

  2. Beihang University, Beijing, China

    Xiao Zhang

  3. Guangzhou University, Guangzhou, China

    Jin Li

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Zang, H., Li, Q., Chen, A., Chen, J., Zhan, S. (2019). Shuffle Dense Networks for Biometric Authentication. In: Vaidya, J., Zhang, X., Li, J. (eds) Cyberspace Safety and Security. CSS 2019. Lecture Notes in Computer Science(), vol 11982. Springer, Cham. https://doi.org/10.1007/978-3-030-37337-5_12

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  • DOI: https://doi.org/10.1007/978-3-030-37337-5_12

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-37336-8

  • Online ISBN: 978-3-030-37337-5

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