Face recognition technology based on video surveillance, is a multidisciplinary challenging subject which is the integration of pattern recognition, video image processing and computer vision. It has a wide range of applications and promising prospects, and has become a research hotspot in recent years, because it provides a highly reliable and convenient identification recognition way. This thesis puts forward a fast way to identify users in a wide range of video surveillance by face recognition technology. The main content of this research includes quickly and accurately locating the face region in the video image, intercepting face photos and extracting facial features. The system is able to be compatible with different types of cameras, real-time monitor the state of the cameras, reconnect with the cameras automatically and quickly when the line drops, schedule server resources effectively and search face s in a large face database efficiently. Going through demand analysis, summary design and detailed design, this thesis designed and implemented a large-scale facial-recognition surveillance system using C# and C + + programming language. The system consists of 5 subsystems, which are face data acquisition system responsible for acquiring the images through decoding video stream, comparison of face features system responsible for calculating the similarity of two facial features, load balancing system responsible for scheduling hardware resources, the Web-based database management system responsible for the adding, modifying, searching, deleting of the face library and maintaining logs of the whole system, and blacklist alarm system responsible for alarming when finding anyone suspicious. Finally, it indicates that the system corresponds with the design requirements through testing. And the system is prepared for the further practical applications.
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