Aiming at the problem that the recognition accuracy is not high in the field of expression recognition

an expression recognition algorithm based on the convolutional neural network and introducing the attention mechanism of the feature channel and the central loss function is proposed. Using VGG11 as the main frame of the network

the feature channel attention mechanism is introduced. After each convolutional layer

a squeeze-andexcitation（ SE） block is added to make the model learn the importance of different feature channels

which improves the sensitivity of the model to the feature channel and further enhance the feature representation ability of the model. At the same time

the neural network is supervised and trained by the center loss function combined with the Soft Max loss function

which effectively increases the distance between inter-classes

ensures the tightness of the intra-class

and improves the recognition accuracy of the model. By testing on the CK + dataset

the experimental results show that the recognition accuracy of the improved model reaches 97. 07 %

which is higher than other typical algorithms.