Facial Expression Recognition Based on Hybrid Attention Mechanism

With the rapid development of science and technology, facial expression technology is gradually applied to many aspects of human life. Through the research of facial expression recognition technology, this paper discusses the application of facial expression recognition in intelligence education. This paper presents a facial expression recognition model combining Attention Mechanism and Convolutional Neural Networks. This model combines channel attention and spatial attention, and can quickly find the places that need attention in the image. It extracts important features in the image according to the weight of information, weakens the useless information, improves the efficiency of network operation, and improves the drawbacks of traditional convolutional neural networks. CK+ dataset is used to cross-validation the model. The data is divided into training set and validation set in a 4:1 ratio. The samples are normalized first, then the normalized data is input into the model, and the validity of the model is verified by a comparison test. The experimental results show that the model proposed in this paper has better efficiency and robustness than other models, and can achieve better results in the actual situation and meet the actual needs of human beings.