FaceTrack: Tracking and summarizing faces from compressed video

In this paper, we present FaceTrack, a system that detects, tracks, and groups faces from compressed video data. We introduce the face tracking framework based on the Kalman filter and multiple hypothesis techniques. We compare and discuss the effects of various motion models on tracking performance. Specifically, we investigate constant-velocity, constant-acceleration, correlated-acceleration, and variable-dimension-filter models. We find that constant-velocity and correlated-acceleration models work more effectively for commercial videos sampled at high frame rates. We also develop novel approaches based on multiple hypothesis techniques to resolving ambiguity issues. Simulation results show the effectiveness of the proposed algorithms on tracking faces in real applications.