## 1 Introduction
in 2018 improvesShuffleNet and proposes four principles for designing lightweight networks: the same channel width minimizes the memory access cost (MAC), too many group convolutions increase the memory access cost (MAC), fragmentation operations within the network reduce parallelism, and element-by-element operations increase the memory consumption. According to these four principles, ShuffleNetV2 introduces channel split operation, replaces group convolution in ShuffleNet with point convolution, and postdates the channel mixing operation. In 2020, [PERSON] et al. [30] proposed the GhostNet model, in which they found that the feature maps generated by convolutional neural networks usually contain rich or even redundant feature maps, and thus these redundant feature maps can be generated directly using constant mapping, so that a large number of feature maps can be obtained with a small amount of computation. In 2019, [PERSON] et al. [31] proposed the EfficientNet model, where they used the Neural Architecture Search (NAS) technique to simultaneously explore the effects of input resolution, network depth, and network width on accuracy. There are also many scholars who, based on these lightweight models, have proposed improved algorithms with considerable effects for some specific application scenarios. For example, [PERSON] et al. [32] propose an improved strategy for the MobileNetV2 neural network(I-MobileNetV2) in response to problems such as large parameter quantities in existing deep convolutional neural networks and the shortcomings of the lightweight neural network MobileNetV2 such as easy loss of feature information, poor real-time performance, and low accuracy rate in facial emotion recognition tasks. To avoid the overfitting problem of the network model and improve the facial expression recognition effect of partially occluded facial images. [PERSON] et al. [33] propose an improved facial expression recognition algorithm based on MobileViT. Firstly, in order to obtain features that are useful and richer for experiments, deep convolution operations are added to the inverted residual blocks of this network, thus improving the facial expression recognition rate. Then, in the process of dimension reduction, the activation function can significantly improve the convergence speed of the model, and then quickly reduce the loss error in the training process, as well as to preserve the effective facial expression features as much as possible and reduce the overfitting problem. In study [34] as it is difficult to highlight the features of facial expressions in the study of global faces, due to the unique subtleties and complexity of facial expressions. To improve the robustness of expression recognition in natural environments and optimize model parameters, a lightweight facial expression recognition method based on multiregion fusion is proposed, which integrates local details and global features to realize a combination of coarse and fine granularity, thus improving the model's efficacy in discriminating subtle changes in expressions. Study [35] presents an expression recognition method based on parallel CNN. Firstly, a series of preprocessing operations is performed on facial expression images. Then, a CNN with two parallel convolution and pooling structures, which can extract subtle expressions, is designed for facial expression images. This parallel structure has three different channels, in which each channel extract different image features and fuse the extracted features, finally, the previously merged features are sent to the Soft Max layer for expression classification. [PERSON] et al. [36] randomly clipped face faces based on face key points and fed the clipping results into the network, which had many duplicate clipping regions although local details were considered. [PERSON] et al. [37] propose POSTER++. It improves POSTER in three directions: cross-fusion, two stream, and multi-scale feature extraction.