Face recognition's grand challenge: uncontrolled conditions under control

The number of cameras increases rapidly in squares, shopping centers, railway stations and airport halls. There are hundreds of cameras in the city center of Amsterdam. This is still modest compared to the tens of thousands of cameras in London, where citizens are expected to be filmed by more than three hundred cameras of over thirty separate Closed Circuit Television (CCTV) systems in a single day [84]. These CCTV systems include both publicly owned systems (railway stations, squares, airports) and privately owned systems (shops, banks, hotels). The main purpose of all these cameras is to detect, prevent and monitor crime and anti-social behaviour. Other goals of camera surveillance can be detection of unauthorized access, improvement of service, fire safety, etc. Since the terrorist attack on 9/11, detection and prevention of terrorist activities especially at high profiled locations such as airports, railway stations, government buildings, etc, has become a new challenge in camera surveillance. In order to process all the recording from CCTV systems, smart solutions are necessary. It is unthinkable that human observers can watch all camera views and analyzing the surveillance footage afterward is a time consuming task. So the great challenge is the automatic selection of interesting recordings. For instance, focussing on well-known shoplifters instead of the shop owner behind the counter. In these cases, the identity of a person gives importantinformation about the relevance of the scene. In order to establish the person's identity, camera surveillance can be combined with automatic face recognition. This allows us to search for possible well-known offenders automatically. Combining face recognition with CCTV systems is difficult, because of the low resolution of recordings and the changing appearance of faces through different scenes. This research focusses on solving some of the fundamental technical problems, which arise when performing face recognition on video surveillance footage. In order to solve these problems, techniques from research on computer vision, image processing and pattern classification are used. These techniques are used to identify a person based on unique biological or behavioral characteristics (biometrics). In this case, the biometric is the face, other famous examples of biometrics are fingerprint and the iris. To recognize the face in surveillance footage, we investigate effects which resolution and illumination can have on existing face recognition systems. We also developed technical methods to improve the recognition rates under these conditions.