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3D Face Recognition Based on Hybrid Data

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11499))

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Abstract

Unlike 2D face recognition (FR), the problem of insufficient training data is a major difficulty in 3D face recognition. Traditional Convolutional neural networks (CNNs) can not comprehensively learn all proper filters for FR applications. We embed a handcrafted feature map into our CNN framework—A hybrid data representation is proposed for 3D face. Furthermore, we use a Squeeze-Excitation block to learn the weights of data channels from training face datasets. To overcome the bias of training model based on a small 3D dataset, transfer learning is applied by fine-turning pre-training models, which is trained based on a large 2D face datasets. Tests show that, under challenge conditions such as expression and occlusion, our method outperforms other state-of-the-art methods and can run in real-time.

Supported by The National Natural Science Foundation of China (61876158), Sichuan Science and Technology Program (2019YFS0432).

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Authors and Affiliations

  1. School of Computer and Software, Jincheng College of Sichuan University, Chengdu, 611731, China

    Xinxin Li

  2. School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China

    Xun Gong

Authors
  1. Xinxin Li
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  2. Xun Gong
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Correspondence to Xun Gong .

Editor information

Editors and Affiliations

  1. University of Debrecen, Debrecen, Hungary

    Tamás Mihálydeák

  2. Southwest Petroleum University, Chengdu, China

    Fan Min

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Guoyin Wang

  4. Indian Institute of Technology Kanpur, Kanpur, India

    Mohua Banerjee

  5. Fujian Normal University, Fuzhou, China

    Ivo Düntsch

  6. University of Rzeszów, Rzeszow, Poland

    Zbigniew Suraj

  7. University of Milano-Bicocca, Milan, Italy

    Davide Ciucci

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Li, X., Gong, X. (2019). 3D Face Recognition Based on Hybrid Data. In: Mihálydeák, T., et al. Rough Sets. IJCRS 2019. Lecture Notes in Computer Science(), vol 11499. Springer, Cham. https://doi.org/10.1007/978-3-030-22815-6_35

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  • DOI: https://doi.org/10.1007/978-3-030-22815-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22814-9

  • Online ISBN: 978-3-030-22815-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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