A Facial Expression Recognition Algorithm Based on Improved VGG16 Network
When the neural network recognizes facial expressions,the classification loss function used is mainly the cross-en-tropy loss function,which leads to the problem that the network has a low recognition rate for different facial expression categories.The category attention mechanism and context awareness pyramid are introduced into the VGG16 network to generate a category loss function,which together with the cross-entropy loss function is used as the loss function for network training,so as to improve the accuracy of facial expression recognition of the network.The experimental results show that the improved VGG16 network has a high-er facial expression recognition rate on the facial expression datasets RAF-DB and FERPLUS than the original VGG16 network.
convolutional neural networkfacial expression recognitioncategory attentionfeel the wild
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