The constantly decreasing  price of surveillance cameras and the notion that more surveillance equals more security have lead to a very large number of surveillance cameras mounted in both public and private spaces. It is for example estimated that 200,000 surveillance cameras are in operation in Denmark and 5,000,000 in the United Kingdom . It is unrealistic to have personnel watching and analysing the extreme amount of video as it is being acquired. Hence the function of the cameras is mostly preventive or to record video for later analysis if need be.

Automated analysis of such video streams is a hot research topic, but so far without much success for general purposes. One way forward is to concentrate on specific applications and possibly accept constrained scene conditions. Face recognition, or at least generation of frontal facial images of persons from surveillance videos, is one important “specific” application worth pursuing.

Commercial face recognizers are currently in operation around the world. They operate by matching a camera image with known faces in a database. For controlled situations, e.g., for access control, persons face the camera and good quality images can be captured for high performance face recognition. For video recorded by surveillance cameras current state-of-the-art recognizers fail due to poor quality of the images, i.e. low resolution, motion blur due to head movement, non-frontal face image, strange facial expressions etc.

Objective: This project will aim at bridging the gab between poor quality surveillance video and technologies processing faces (like a face recognizer), which require good quality images of the face. A successful project will allow for, e.g., automatic and real-time recognition of faces in a standard surveillance camera setup.

Content: The project contains three parts; 1) figure-ground segmentation of moving objects in video, 2) control active ptz cameras to focus on and capture video of faces, and 3) obtaining a good quality face image.