Aiming at the problems of low calculation efficiency and poor robustness of the existing face recognition system, this study proposed a face recognition system based on front and back interaction, including client, database, and server. First, a GrabCut-based facial Region Of Interest (ROI) extraction algorithm was proposed for the client end. Second, the extracted ROI is transmitted to the server, and the ResNet network is used on the server to extract facial feature points according to the ROI. Finally, the facial feature points extracted from the server were returned to the client, and the client performs Euclidean distance matching between this information and the feature points that were pre-stored in the database to obtain the face recognition result. The experiments were performed on the CeleA database and random videos, and the results show that the proposed ROI extraction algorithm significantly improves the accuracy and robustness of face recognition. Moreover, compared with the traditional non-interactive structure, the front and back interactive structure of the system greatly improves the computational efficiency of face recognition.