The Implementation of Personal Monitoring System Using Histogram of Gradient, Haar Classifier and Eigenfaces for Human Detection and Recognition

The development of personal monitoring system aims as an initial study for home surveillance system that covers only frontal view of human face. This study focuses on a combination of computer vision analysis and video surveillance which has a wide range of application in monitoring and surveillance system. In general, the monitoring and surveillance system is vital in reducing crime cases as well as to protect public safety. Moreover, the development of personal monitoring focuses on two objectives; detecting the existence of human and performing face recognition for the detected human, which covers the pre-processing, processing and post processing stages. In the processing stage, background subtraction and Histogram of Gradient texture analysis techniques are used for human detection process. On the other hand, the Haar cascade and Eigenface recognizer techniques are used in the face recognition process. As the results, the combinations of these techniques from two phases produce a better human identification and faster detection. The impact of the personal monitoring development system shows a successful achievement on human recognition as well as an accurate identification of personal monitoring.