Enhancing lifetime of visual sensor networks with a preprocessing-based multi-face detection method

Recently, advances in hardware such as CMOS camera nodes have led to the development of Visual Sensor Networks (VSNs) that process sensed data and transmit the useful information to the base station for completing subsequent tasks. Today, object detection and sending useful information to the base station for object recognition is emerged as an important challenging issue in VSNs. Our investigations show that the face’s information is adequate for completing object recognition. According to literature, many approaches have been proposed for object detection and sending useful information to the base station to be completed subsequent tasks like object recognition. However, in most of them, lack of preprocessing methods in camera nodes causes network to be faced with large volume of data. For example when there is more than one object within the each camera node filed-of-view, conventional works deliver empty spaces among objects to the base station. Also, most of them send whole information about each object to the base station, while sending only face’s information of each object is adequate for completing object recognition. Therefore, in this paper, a preprocessing method in camera nodes named Preprocessing-based Multi-Face Detection (PMFD) is proposed. Our method works based on the extracting bounding box of each object’s face, using Boosting-based face detection algorithm, and sending only the faces’ information to the base station. The simulation results show that PMFD method has acceptable preprocessing time complexity and injects low volume of traffic into the network. Consequently, PMFD method prolongs the network lifetime in comparison with state-of-the-art algorithms.