Real-Time Face Detection and Classification for ICCTV

Real-Time Face Detection and Classification for ICCTV

Brian C. Lovell (The University of Queensland, Australia), Shaokang Chen (NICTA, Australia) and Ting Shan (NICTA, Australia)
Copyright: © 2009 |Pages: 8
DOI: 10.4018/978-1-60566-010-3.ch253
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Data mining is widely used in various areas such as finance, marketing, communication, web service, surveillance and security. The continuing growth in computing hardware and consumer demand has led to a rapid increase of multimedia data searching. With the rapid development of computer vision and communication techniques, real-time multimedia data mining is becoming increasingly prevalent. A motivating application is Closed-Circuit Television (CCTV) surveillance systems. However, most data mining systems mainly concentrate on text based data because of the relative mature techniques available, which are not suitable for CCTV systems. Currently, CCTV systems rely heavily on human beings to monitor screens physically. An emerging problem is that with thousands of cameras installed, it is uneconomical and impractical to hire the required numbers of people for monitoring. An Intelligent CCTV (ICCTV) system is thus required for automatically or semi-automatically monitoring the cameras.
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CCTV Surveillance Systems

In recent years, the use of CCTV for surveillance has grown to an unprecedented level. Especially after the 2005 London bombings and the 2001 terrorist attack in New York, video surveillance has become part of everyday life. Hundreds of thousands of cameras have been installed in public areas all over the world, in places such as train stations, airports, car parks, Automatic Teller Machines (ATMs), vending machines and taxis. Based on the number of CCTV units on Putney High Street, it is “guesstimated” (McCahill & Norris 2002) that there are around 500,000 CCTV cameras in the London area alone and 4,000,000 cameras in the UK. This suggests that in the UK there is approximately one camera for every 14 people. However, currently there is no efficient system to fully utilize the capacity of such a huge CCTV network. Most CCTV systems rely on humans to physically monitor screens or review the stored videos. This is inefficient and makes proactive surveillance impractical. The fact that police only found activities of terrorists from the recorded videos after the attacks happened in London and New York shows that existing surveillance systems, which depend on human monitoring, are neither reliable nor timely. The need for fully automatic surveillance is pressing.

Challenges of Automatic Face

Recognition on ICCTV Systems

Human tracking and face recognition is one of the key requirements for ICCTV systems. Most of the research on face recognition focuses on high quality still face images and achieves quite good results. However, automatic face recognition under CCTV conditions is still on-going research and many problems still need to be solved before it can approach the capability of the human perception system. Face recognition on CCTV is much more challenging. First, image quality of CCTV cameras is normally low. The resolution of CCTV cameras is not as high as for still cameras and the noise levels are generally higher. Second, the environment control of CCTV cameras is limited, which introduces large variations in illumination and the viewing angle of faces. Third, there is generally a strict timing requirement for CCTV surveillance systems. Such a system should be able to perform in near real-time — detecting faces, normalizing the face images, and recognizing them.

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