A massive face image retrieval method based on depth feature clustering is proposed to overcome the long time retrieving in a huge scale face image database. Firstly

the convolutional neural network model is trained for face classification by face image training set. Based on it

the triplet loss method is used to fine-tune the model so that the network can be more efficient to extract high-level semantic features and construct a more representative depth features of face image. Secondly

the K-means clustering algorithm is used to cluster the extracted depth features

so that face images of the same person can be divided into the same cluster

and then similarity matching of face images is performed in the corresponding clusters to perform the retrieval task. In order to further improve the performance of system retrieval

the face image feature fusion query expansion method is proposed to fuse the depth features of the face image to be retrieved. Through exhaustive experimental verification on two face retrieval datasets (Celebrities Face Set and Labeled Faces in the Wild dataset)

the results show that the proposed method can significantly reduce the retrieval range of massive face image database

thus effectively increasing the face image retrieval speed while ensuring similar retrieval accuracy.