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.
biometrics (access control);expectation-maximisation algorithm;feature extraction;hidden Markov models;image motion analysis;infrared detectors;lenses;optical arrays;pyroelectric detectors;