Facial expression recognition method based on multi-level feature fusion of high-resolution images

To improve the accuracy of facial expression recognition, the paper designs a facial expression recognition method based on multi-level feature fusion of high-resolution images. Firstly, smooth the noise and texture in the facial image and perform enhancement processing. Secondly, extract multi-level features of facial images, and then fuse multi-level features through reverse solving. Thirdly, extract the attributes of different regions of the face and assign them to the corresponding representation data. Then, extract decoupled data of facial expressions based on feature fusion results. Lastly, compare decoupled representation and representation data to complete the facial expression recognition. The experiment shows that the geometric mean of the recognition results obtained by this method is between 0.963 and 0.989, and the similarity of the feature vectors is between 0.972 and 0.988, indicating that this method can accurately output facial expression recognition results.