The face image data that can be obtained in the field of public security has grown rapidly. The traditional manual method to identify people has large workload, poor real-time performance, and low accuracy. This study designs a large-scale real-time face retrieval system. The system implements the real-time storage and retrieval of captured face images through the distributed platform Storm, and implements the storage and maintenance of large-scale unstructured face data through the distributed storage system HBase. The results of multiple experiments show that the system has a good speedup, good scalability, and real-time performance in the application scenarios of large-scale face image data retrieval.