Face recognition from surveillance-quality video

In this dissertation, we develop techniques for face recognition from surveillance-quality video. We handle two specific problems that are characteristic of such video, namely uncontrolled face pose changes and poor illumination. We conduct a study that compares face recognition performance using two different types of probe data and acquiring data in two different conditions. We describe approaches to evaluate the face detections found in the video sequence to reduce the probe images to those that contain true detections. We also augment the gallery set using synthetic poses generated using 3D morphable models. We show that we can exploit temporal continuity of video data to improve the reliability of the matching scores across probe frames. Reflected images are used to handle variable illumination conditions to improve recognition over the original images. While there remains room for improvement in the area of face recognition from poor-quality video, we have shown some techniques that help performance significantly.