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

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Fang, J-S; Hao, Q; Brady, DJ; Guenther, BD; Hsu, KY

Published Date

  • July 2006

Published In

Volume / Issue

  • 14 / 15

Start / End Page

  • 6643 - 6658

PubMed ID

  • 19516845

Pubmed Central ID

  • 19516845

Electronic International Standard Serial Number (EISSN)

  • 1094-4087

International Standard Serial Number (ISSN)

  • 1094-4087

Digital Object Identifier (DOI)

  • 10.1364/oe.14.006643

Language

  • eng