This paper reviews the development of face recognition based on deep learning in the field of biometrics. Firstly, the basic application of face recognition and the definition of the deep learning model is explained. In addition, the research overview and application are summarized, such as face recognition method based on convolution neural network (CNN), deep nonlinear face shape extraction method, face-based robustness modeling based on deep learning, fully automatic face recognition in constrained environments, face recognition based on deep learning video monitoring, low resolution face recognition based on deep learning, and other deep learning of the face information recognition; analysis of the current face recognition technology in the deep learning applications in the problems and development trends. Finally, it is concluded that the deep learning can learn to get more useful data and can build a more accurate model. However, there are some shortcomings in deep learning, such as the length of the training model, the need for continuous iteration to model optimization, being difficult to guarantee the optimal global solution, which also needs to continue to explore in the future.
