Abstract: In order to improve the performance of the face feature extraction network and then the accuracy of the face recognition algorithm, this paper studies the network of the face feature extractor based on the convolutional neural network and proposes SFRNet (Sparse Feature Reuse Network). First, based on the three innovations of sparse feature reuse, hybrid feature fusion, and Center-Gaussian pooling, the network structure of SFRNet is given. Then, experiments were performed on the image classification dataset ImageNet, the face recognition dataset LFW (Labeled Faces in the Wild), and MegaFace, respectively, to verify the feature extraction capabilities of SFRNet in the general scene and the specific scene of face recognition. Experiments show that the SFRNet designed in this article not only has a small amount of calculation and parameters, but also can effectively extract facial features and has strong generalization ability in general scenes.