Smart Surveillance System For Face Recognition

Abstract

Smart surveillance system refers to video level processing techniques for identification of unwanted (terrorist) faces from real time video. Video object segmentation is an important part of real time surveillance system. For any video segmentation algorithm to be suitable in real time, must require less computational load. The dissertation work presented here is divided into two main parts: (1) Face Detection, (2) Matching of detected faces with the unwanted faces (terrorist). To detect a face from video frame we use Camshafts algorithm that gives us surfaces, which can be used by Sift technique for feature extraction and matching with the faces of unwanted person (terrorist). Further for identifying object as a face from the video of a stationary camera, there are different face detection techniques. Once the face detection in video frame is done then the feature extraction and matching to be done. When face matches with any of unwanted face then the system raise the alarm, so that at sensitive areas like airport, railway station, tourist place etc. the security guard or other person get alert tone, thus they can take necessary action and make system secure. Many developed and developing country are using smart surveillance system for viewing the unwanted faces remotely.