Multi-Factor Approach to Improving Recognition Performance in Surveillance-Quality Video

Face recognition from video typically involves two steps: face detection and face recognition. One of the problems associated with poor face recognition results is poor face detection. In this work, we devise a three-step strategy to prune a set of frames in a probe video to that subset in which the face is actually found and in which the matching scores are more reliable. We also exploit temporal continuity of video frames to improve recognition by weighting the match scores based on the results in previously seen frames. We show that though this is a very challenging real-world dataset, by combining these different approaches we can improve recognition over the baseline case.