In order to solve the problems that the existing facial expression recognition feature extraction is susceptible to be affected by background and individual factors

gaps within classes are large

similarity between classes are high

and real-time performance is poor

a lightweight facial expression recognition method based on attention network with efficient channels is proposed.This method is based on the depth separable convolution to improve the linear bottleneck structure to reduce network complexity and prevent over-fitting.The depth of the feature map is combined with spatial information by designing efficient attention module

focusing more on the extraction of important features

and adopting a joint loss function to reduce the feature difference within the same expression category

expand the feature spacing between different expression categories

so that the network has better feature discrimination effect.The recognition rate of this method in the FER—2013 and CK+ datasets reacheds 73.3 % and 97.9 %

which has better recognition performance than many current newer methods.