Abstract
With the development of artificial intelligence, facial expression recognition based on deep learning has become a current research hotspot. The article analyzes and improves the VGG16 network. First, the three fully connected layers of the original network are changed to two convolutional layers and one fully connected layer, which reduces the complexity of the network; Then change the maximum pooling in the network to local-based adaptive pooling to help the network select feature information that is more conducive to facial expression recognition, so that the network can be used on the facial expression datasets RAF-DB and SFEW. The recognition rate increased by 4.7% and 7% respectively.
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