In order to solve the problem of face image retrieval in the field of computer vision, a face image retrieval method based on the deep features is proposed. Firstly, the convolutional neural network model is trained for face classification by face image training data set. Based on this, the triplet loss method is used to fine-tuning the trained face classification network model so that the network can be more efficient to extract face features of different people and construct efficient feature vectors for preliminary face retrieval filtering stage. In order to further improve the performance of system retrieval, the one-stage query expansion method is proposed to reconstruct the eigenvectors of face images to be retrieved. Through exhaustive experimental verification on two public face datasets (CASIA-3D FaceV1 and Labeled Faces in the Wild dataset), the results show that the face image retrieval method based on deep features improves the accuracy of the retrieval results significantly. Moreover, this method is simple and reliable, and can quickly realize the task of face retrieval.