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A Lightweight Facial Expression Recognition Network Based on Dense Connections

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Knowledge Management in Organisations (KMO 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1593))

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Abstract

Facial expression is one of the most representative signals for human emotional states and intentions. Facial expression recognition has attracted increasing attention in academia and industry, and has been wide used in robotics, intelligent security, medical monitoring, educational evaluation, driving fatigue monitoring, etc. This paper proposes a lightweight network Dense-MobileNet. In which, a DenseDW-block for feature reuse is designed and embedded into MobileNetV1 for better accuracy and less computation. The width selection and comparison experiments are used on the widely used Real-World Affective Face Database (RAF-DB) to choose the best network parameters and to validate the effectiveness of the proposed Dense-MobileNet. The results show that: 1) Among the three proposed sub networks Dense-MobileNet-1, Dense-MobileNet-2, Dense-MobileNet-3, the Dense-MobileNet-2 has the best accuracy of 82.4%. 2) Comparing with MobileNetV1, the recognition accuracy of our model is improved by 2.5%, the number of parameters is reduced by 45.7%, and the amount of computation is reduced by 66.73%. As a lightweight network with better accuracy and less computation, the proposed Dense-MobileNet is suitable for facial expression recognition on mobile terminals and edge devices. The proposed DenseDW-block serving as a feature reuse module can be used to design or optimize similar CNN to improve accuracy and accelerate computation.

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Acknowledgments

This research was supported by the National Key R&D Program of China under Grant No. 2020YFB1707700.

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Authors and Affiliations

  1. School of Computer Science and Technology, Donghua University, Shanghai, China

    XiaoKang Xu, Ran Tao, Xiangyang Feng & Ming Zhu

Authors
  1. XiaoKang Xu
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  2. Ran Tao
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  3. Xiangyang Feng
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  4. Ming Zhu
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Corresponding authors

Correspondence to XiaoKang Xu or Ran Tao .

Editor information

Editors and Affiliations

  1. Staffordshire University, Stoke-on-Trent, UK

    Lorna Uden

  2. University of Kaohsiung, Kaohsiung, Taiwan

    I-Hsien Ting

  3. FernUniversität in Hagen, Hagen, Germany

    Birgit Feldmann

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Xu, X., Tao, R., Feng, X., Zhu, M. (2022). A Lightweight Facial Expression Recognition Network Based on Dense Connections. In: Uden, L., Ting, IH., Feldmann, B. (eds) Knowledge Management in Organisations. KMO 2022. Communications in Computer and Information Science, vol 1593. Springer, Cham. https://doi.org/10.1007/978-3-031-07920-7_27

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  • DOI: https://doi.org/10.1007/978-3-031-07920-7_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-07919-1

  • Online ISBN: 978-3-031-07920-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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