Abstract：In order to overcome the difficulty of extracting expression features from single neural network,and the complexity of training process and redundancy of parameters caused by stacking deep structure,a dual channel model of lightweight CNN and convolutional self-encoder pretraining model is proposed.In the lightweight CNN channel,the structure with residual block and depth separable convolution is used for feature extraction,and the amount of model parameters is reduced.The channel of attention mechanism is used to enable the channel to learn more useful features;at the same time,the convolution self-encoder is used for unsupervised preprocessing of the facial expression image input,which makes the features extracted from the model more diversified.The experimental results show that the recognition rate is 72.70% and 97.50% respectively on fer 2013 and CK+expression data sets.Compared with the related methods,the proposed model has a higher recognition rate while ensuring less parameters.
2022, Vol. 43 
