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Face Detection Algorithm in Classroom Scene Based on Deep Learning

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Advances in Computer Graphics (CGI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13443))

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Abstract

With informatisation progresses in education, the application of artificial intelligence technology in education is increasing day by day. Although some breakthroughs have been made, the application in specific scenes (such as classroom scenes) faces many difficulties, such as small target detection, severe occlusion, etc. We propose a target detection algorithm based on video data for the classroom scene, which combines the optical flow information to improve the accuracy and alleviate the impact of occlusion. We also put forward the counting method suitable for classroom scenes, which can be used as the evaluation standard of attendance rate, head-up rate, and other indicators in classroom quality evaluation.

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Author information

Authors and Affiliations

  1. Beijing Institute of Technology, No. 5 Yard, Zhong Guan Cun South Street, Beijing, China

    Yi Zhang & Chongwen Wang

Authors
  1. Yi Zhang
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  2. Chongwen Wang
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Corresponding author

Correspondence to Chongwen Wang .

Editor information

Editors and Affiliations

  1. University of Geneva, Geneva, Switzerland

    Nadia Magnenat-Thalmann

  2. Bournemouth University, Poole, UK

    Jian Zhang

  3. University of Sydney, Sydney, NSW, Australia

    Jinman Kim

  4. University of Crete, Heraklion, Greece

    George Papagiannakis

  5. Shanghai Jiao Tong University, Shanghai, China

    Bin Sheng

  6. Swiss Federal Institute of Technology, Lausanne, Switzerland

    Daniel Thalmann

  7. University of Calgary, Calgary, AB, Canada

    Marina Gavrilova

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© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Zhang, Y., Wang, C. (2022). Face Detection Algorithm in Classroom Scene Based on Deep Learning. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2022. Lecture Notes in Computer Science, vol 13443. Springer, Cham. https://doi.org/10.1007/978-3-031-23473-6_20

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  • DOI: https://doi.org/10.1007/978-3-031-23473-6_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23472-9

  • Online ISBN: 978-3-031-23473-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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