As a popular research direction in the field of artificial intelligence, FER is the most direct and effective way for computers to access human emotions. It occupies a key technical position in human-computer interaction, intelligent medical care, fatigue driving, and other R&D topics. In order to meet the application requirements of high recognition rate, the structure of FER deep learning network becomes more and more complex, occupying a large amount of computing resources and storage space, which seriously affects the real-time requirements of the algorithm. This paper focused on the problem of how to guarantee the accuracy of the model while effectively improving its computational speed. Firstly, it introduced the important datasets for expression recognition using lightweight networks. Secondly, it analyzed the classical lightweight network models used for facial expression recognition. Thirdly, it described the principles, characteristics, and applicable scenarios of the main network lightweighting methods. Finally, it summarized the problems and challenges of lightweight networks in facial expression recognition research and looked forward to the future research direction.

[2023-11-14] Accepted Paper /doc/2023.07.0287/v1

[2024-03-05] Printed Article /doc/2023.07.0287/v2