针对ResNet18网络模型在面部表情识别时存在网络模型大、准确率低等问题，提出了一种基于注意力机制ResNet轻量网络模型（Multi-Scale CBAM Lightweight ResNet，MCLResNet），能够以较少的参数量、较高的准确率实现面部表情的识别。首先，采用ResNet18作为主干网络提取特征，引入分组卷积减少ResNet18的参数量；利用倒残差结构增加网络深度，优化了图像特征提取效果。其次，将CBAM（Convolutional Block Attention Module）通道注意力模块中的共享全连接层替换为1×3的卷积模块，有效减少了通道信息的丢失；在CBAM空间注意力模块中添加多尺度卷积模块获得了不同尺度的空间特征信息。最后，将多尺度空间特征融合的CBAM模块（Multi-Scale CBAM，MSCBAM）添加到轻量的ResNet模型中，有效增加了网络模型的特征表达能力，另外在引入MSCBAM的网络模型输出层增加一层全连接层，以此增加模型在输出时的非线性表示。该模型在FER2013和CK+数据集上的实验结果表明，本文提出的模型参数量相比ResNet18下降82.58%，并且有较好的识别准确率。

Aiming at the problems of large network model and low accuracy of ResNet18 network model in facial expression recognition, a Lightweight ResNet based on multi-scale CBAM (Convolutional Block Attention Module) attention mechanism (MCLResNet) is proposed, which can realize facial expression recognition with less parameters and higher accuracy. Firstly, ResNet18 is used as the backbone network to extract features, and group convolution is introduced to reduce the parameters quantity of ResNet18. The inverted residual structure is used to increase the network depth and optimized the effect of image feature extraction. Secondly,the shared fully connected layer in the channel attention module of CBAM is replaced with a 1×3 convolution module,^{which effectively reduces the loss of channel information. The multi-scale convolution module is added to the CBAM spatial attention module to obtain spatial feature information at different scales. Finally, multi-scale CBAM module (MSCBAM) is added to the lightweight ResNet model, which effectively increases the feature expression ability of the network model. In addition, a fully connected layer is added to the output layer of the network model introduced into MSCBAM, so as to increase the nonlinear representation of the model at the output. The experimental results of the model on FER2013dataset and CK+ dataset show that the parameters quantity of the model proposed in this paper is reduced by 82.58% compared with ResNet18,and the recognition accuracy is better.}