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Facial Expression Recognition via ResNet-18

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Multimedia Technology and Enhanced Learning (ICMTEL 2021)

Abstract

As an important part of human-computer interaction, facial expression recognition has become a hot research topic in the fields of computer vision, pattern recognition, artificial intelligence, etc., and plays an important role in our daily life. With the development of deep learning and convolutional neural network, the research of facial expression recognition has also made great progress. Moreover, in the current face emotion recognition research, there are problems such as poor generalization ability of network model. The extraction of traditional facial expression recognition features is complex and the effect is not ideal. In order to improve the effect of facial expression recognition, we propose a feature extraction method for deep residual network, and use deep residual network ResNet-18 to extract the features of the data set. Through the experimental simulation of the specified data set, it can be proved that this model is superior to state-of-the-art methods model.

B. Li and R. Li—Those two authors contributed equally to this paper, and should be regarded as co-first authors.

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Bin Li

  2. Nanjing Foreign Language School, Nanjing, Jiangsu, China

    Runda Li

  3. Department of Electrical Engineering, Federal University of Santa Catarina, Florianópolis, Brazil

    Dimas Lima

Authors
  1. Bin Li
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  2. Runda Li
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  3. Dimas Lima
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Correspondence to Dimas Lima .

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Editors and Affiliations

  1. Hunan Normal University, Changsha, China

    Weina Fu

  2. University of Jinan, Jinan, China

    Yuan Xu

  3. University of Leicester, Leicester, UK

    Shui-Hua Wang

  4. University of Leicester, Leicester, UK

    Yudong Zhang

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© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Li, B., Li, R., Lima, D. (2021). Facial Expression Recognition via ResNet-18. In: Fu, W., Xu, Y., Wang, SH., Zhang, Y. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 388. Springer, Cham. https://doi.org/10.1007/978-3-030-82565-2_24

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  • DOI: https://doi.org/10.1007/978-3-030-82565-2_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-82564-5

  • Online ISBN: 978-3-030-82565-2

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