Abstract: Aiming at the problem that the existing deep convolutional neural network has a large amount of parameters, which leads to the limitation of facial expression recognition scenes, this paper proposes a facial expression recognition model based on improved lightweight convolutional neural network. The model takes MobileNetV2 lightweight feature extraction network as the main framework, by compressing the network width factor and the global dimension, the number of network parameters and the amount of computation are reduced. SandGlass block is introduced to improve the reverse residual module in this network, and reduce the loss of feature information during network transmission. At the same time, the efficient channel attention mechanism is embedded to improve the network's ability to extract feature information. Experiments were carried out on the facial expression data sets FER2013 and CK+. The facial expression accuracy rate of the proposed network reaches 68.96% and 95.96%, which are 1.06% and 6.14% higher than that of MobileNetV2 respectively, and the number of parameters are decreased by 82.28%. Experimental results verify the effectiveness of the improved network model.

Key words: facial expression recognition, lightweight network, MobileNetV2, inverted residual block, channel attention