The Use of Fuzzy Set Classification for Pattern Recognition of the Polygraph

Abstract

Polygraph tests are a widely used method to distinguish between truth, and deception. Polygraph charts are usually analyzed by human interpreters. However, computer algorithms are now being developed to score the tests or verify the results. These methods are based on statistical classification techniques. In this study a number of time, frequency and correlation domain features were selected and used. The fuzzy K-nearest neighbor algorithm was used to classify the polygraph charts, a correct classification of ninety-one percent was obtained for a set of one hundred case files supplied by the NSA.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1993
Accession Number
ADA279148

Entities

People

  • Benjamin Knapp
  • Eric Jacobs
  • Mitra Dastmalchi
  • Shahab Layeghi

Organizations

  • San José State University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Algorithms
  • Blood Volume
  • Computer Programs
  • Computers
  • Detection
  • Electrical Engineering
  • Feature Extraction
  • Frequency Domain
  • Fuzzy Logic
  • Fuzzy Sets
  • Information Science
  • Lie Detectors
  • Machine Learning
  • Pattern Recognition
  • Set Theory
  • Supervised Machine Learning

Readers

  • Computer Vision.
  • Regression Analysis.
  • Sensor Fusion and Tracking Systems.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Translation