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Heterogeneous Kernel Based Convolutional Neural Network for Face Liveness Detection

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Bio-inspired Computing: Theories and Applications (BIC-TA 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1160))

Abstract

Liveness detection is a part of living biometric identification. While the face recognition system is promoted, it is also vulnerable to deceived and attacked from fake faces. Face liveness detection in traditional method needs network take long time to training and easy to appear over-fitting. Therefore, this paper proposed a Heterogeneous Kernel-Convolutional Neural Network (HK-CNN), the method replaces the standard convolutional kernel with heterogeneous convolutional kernel to detect the real face. In the two classic databases of NUAA and CASIA-FASD, the algorithm can improve accuracy and reduce the training cost of the model. Compared with traditional convolutional neural network methods, this algorithm has higher efficiency.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Project Number: 72472081).

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Authors and Affiliations

  1. School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114051, China

    Xin Lu & Ying Tian

Authors
  1. Xin Lu
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  2. Ying Tian
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Corresponding author

Correspondence to Ying Tian .

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Editors and Affiliations

  1. Huazhong University of Science and Technology, Wuhan, China

    Linqiang Pan

  2. Zhengzhou University, Zhengzhou, China

    Jing Liang

  3. Zhongyuan University of Technology, Zhengzhou, China

    Boyang Qu

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Lu, X., Tian, Y. (2020). Heterogeneous Kernel Based Convolutional Neural Network for Face Liveness Detection. In: Pan, L., Liang, J., Qu, B. (eds) Bio-inspired Computing: Theories and Applications. BIC-TA 2019. Communications in Computer and Information Science, vol 1160. Springer, Singapore. https://doi.org/10.1007/978-981-15-3415-7_32

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  • DOI: https://doi.org/10.1007/978-981-15-3415-7_32

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-3414-0

  • Online ISBN: 978-981-15-3415-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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