Pyroelectric Infrared Sensors for Human Identification Using Non-negative Matrix Factorization and BP Neural Network

Abstract

Pyroelectric Infrared (PIR) sensors are excellent devices for wireless sensor network due to its characteristics of low-cost and low-power. PIR sensors are widely used to establish simple but reliable system for detecting targets or triggering alarms. However, processing numerous output data from PIR sensors and correcting the high false generated in the process of classifying and identifying of human targets limit the application scope of PIR sensors. In this paper, a feature extraction and sensor data fusion method to detect and recognize multiple human targets moving in a detection area are presented. Simulation results shows that such approach can reduce computational requirement which indicates that PIR sensors can be used as wireless sensor nodes with limited resources. Additionally, when using the BP neural network, the system can achieve 96% correct identification of individual target data and 90% correct classification of multiple targets mixed data as well.