Facial Expression Recognition Method Based on Improved EfficientNet
In response to the insufficient extraction of expression features in existing network models and the problems of large intra class and small inter class differences in expression data,research was conducted on the extraction methods of global and local features in model training,and an expression recognition method based on improved EfficientNet has been proposed.Firstly,large kernel Fused-MBConv convolutional blocks were used in shallow networks to extract global features.Then,small kernel MBConv convolutional blocks were used in the deep network to extract local features,and combined with the ACON activation function,the LA-EfficientNetB0 network was constructed with EfficientNetB0 as the baseline network.Finally,the effectiveness of the global and local feature extraction methods proposed in this paper was demonstrated by displaying the regions of interest of the original image with the features extracted by different models through Grad-CAM.The results showed that the accuracy of LA-EfficientNetB0 in the FER2013 facial expression dataset reached 71.61%,which is better than the VGG16,ResNet50,EfficientNetB0,and EfficientNetV2B0 network models.
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