Noise Removal Based Query Pre-processing to Improve Face Search Performance in Large Scale Video Databases

In most person search systems, for ease of use, a user is often required to provide only raw images that contain person as query examples without specific face location. As a result, a face detector needs to be used. However, current face detectors are robust to pose, thus using all the detected faces as query can hurt search performance. Therefore, having a good stage for removing bad examples in query can directly lead to better performance on the whole system. In this paper, we focus on analyzing how bad face examples affect person search system. Moreover, we propose an automatic bad-face removal method which is stable to the case where bad faces are dominant in a query. Experiments show that our removal method yields better mAP in both image example and shot example setting compared to that of Peking state of the art system.