Research On Security Video-surveillance System Based On Face Recognition Technology

The video surveillance system based on face recognition has the advantage of concealment and non-contact, so it has received extensive attention in recent years.In this paper,a security video surveillance system which is used face recognition technology has been designed, and it will be used in fixed-background environment.System includes three main parts. Each is described as follows:1.System platform,s design: In this paper, we adopt Microsoft's multimedia software development kits --DirectShow structures in the framework of the whole system,at the meanwhile, the kits can be also used to accomplish video capture and format conversion.2 . Face detection and locating: A composite light compensation strategy is proposed,and a new logarithmic transform algorithm is proposed. According to the character of video surveillance system, a rapid face detection approach based on fixed-background is proposed. The testing results show that the algorithm is fast and the approach is high robust. This approach can be used in video surveillance system well.3.Quick face recognition: The principal component analysis and linear discriminant analysis are introduced. And we have put an improved method against linear discriminant analysis of defect, then use the method of principal component analysis and improved linear discriminant analysis combining to finish feature extraction of face image. Finally we have achieved face recognition using a minimum distance classifier and a three layer BP neural network classifier. Experiments show that properties meet the basic system requirements.The experimental images are all collected by Camera, including single face, muti-faces and different face expression. Experimental environment is the Pentium IV, the PC memory 256M PC. Experimental results demonstrates that as for a small image library ,including 92 individuals face, each size 320×240 pixels, face detection correctness rate has reached to 93.5%.The average detection time is 0.5s. In face recognition stage, when the category is not too much, face recognition correctness rate has reached to more than 95%.And average recognition time is approximately 0.3s in laboratory. The accuracy and time-consuming performance of these two functional modules can meet general requirements of the important things anti-theft surveillance system.