Real-time human identification using a pyroelectric infrared detector array and hidden Markov models

This paper proposes a real-time human identification system using a pyroelectric infrared (PIR) detector array and hidden Markov models (HMMs). A PIR detector array with masked Fresnel lens arrays is used to generate digital sequential data that can represent a human motion feature. HMMs are trained to statistically model the motion features of individuals through an expectation-maximization (EM) learning process. Human subjects are recognized by evaluating a set of new feature data against the trained HMMs using the maximum-likelihood (ML) criterion. We have developed a prototype system to verify the proposed method. Sensor modules with different numbers of detectors and different sampling masks were tested to maximize the identification capability of the sensor system.