Wireless sensor network for community intrusion detection system based on classify support vector machine

A community intrusion detection system based on classify support vector machine (SVM) is presented in this paper. This system is composed of ARM (advanced RISC machines) data acquisition nodes, wireless mesh network and control centre. The data acquisition node uses sensors to collect information and processes them by image detection algorithm, and then transmits information to control centre with wireless mesh network. When there is abnormal phenomenon, the system starts the camera and the classify SVM is used to recognize the face image. The SVM network structure that used for face recognition is established, and we use the genetic algorithm (GA) to optimize SVM parameters, thereby enhancing the convergence rate and the recognition accuracy. With the ability of strong pattern classification and self-learning and well generalization of SVM, the recognition method can truly classify the face. This system resolves the defect and improves the intelligence and alleviates worker's working stress.