In the process of face expression analysis, head pose changes often cause asymmetry in face information, and it is difficult to obtain features that are robust to pose by traditional operations related to cropping and aligning face images only. In order to obtain structured features of faces, a face image frontalization processing method is proposed in the paper. The method maps the detected face landmarks to a new two-dimensional space for frontalization of landmarks, then restores the frontalized landmarks to the original image as new landmarks, and guides the image deformation from the original landmarks to the new landmarks by moving least squares to obtain the frontalized face image. The face images are preprocessed on public RAF-DB and ExpW face expression datasets using the proposed processing method, models are trained in VGG16 and ResNet50 deep learning networks for face expression classification tasks. The effectiveness of the frontalization method in the paper for face expression analysis is evaluated by the accuracy of the classification tasks. The experimental results show that this proposed method outperforms traditional pre-processing methods of deep learning in face expression analysis and can effectively improve the quality of face information.