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Deep learning-based microexpression recognition: a survey

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Abstract

With the recent development of microexpression recognition, deep learning (DL) has been widely applied in this field. In this paper, we provide a comprehensive survey of the current DL-based microexpression (ME) recognition methods. In addition, we introduce a novel dataset based on fusing all the existing ME datasets. We also evaluate a baseline DL for the microexpression recognition task. Finally, we make the new dataset and the code publicly available to the community at https://github.com/wenjgong/microExpressionSurvey.

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Notes

  1. https://github.com/xiaobaishu0097/MEGC2019.

  2. https://github.com/IcedDoggie/DSSN-MER.

  3. https://github.com/bogireddytejareddy/micro-expression-recognition.

  4. https://github.com/christy1206/STSTNet.

  5. https://github.com/CodeShareBot/ACII19-Apex-Time-Network.

  6. https://github.com/IcedDoggie/Micro-Expression-with-Deep-Learning.

  7. https://github.com/quangdtsc/megc2019

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  1. China University of Petroleum (East China), No. 66, Changjiangxi Road, Huangdao, Qingdao, China

    Wenjuan Gong & Zhihong An

  2. Saint Mary’s College of California, 1928 St. Marys Road, Moraga, CA, 94575, USA

    Noha M. Elfiky

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Gong, W., An, Z. & Elfiky, N.M. Deep learning-based microexpression recognition: a survey. Neural Comput & Applic 34, 9537–9560 (2022). https://doi.org/10.1007/s00521-022-07157-w

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