Examining the Reliability of a Retinal Recognition Device as Database Size and the Number of Enrollment Scans are Varied for Applications in Command, Control and Communications (C3)

Abstract

As the amount of sensitive information stored in databases increases due to the current trend to automate Command, Control and Communication C superscript 3 systems, the impact of unauthorized access could be very detrimental to our nation's security. Access control hardware that uses retinal blood vessel pattern recognition may be the solution to the problem. This thesis looks at one retinal pattern recognition device and attempts to determine it's reliability as a function of the data base size stored in memory and the number of enrollment scans averaged together to form the reference template. The database sizes used consisted of 300, 600 or 1200 templates, and the reference templates tested were comprised of 3, 5 or 7 enrollment scans. The applicability of this technology for protecting C superscript 3 systems is discussed. This study employed the Eye Dentify Inc. of 7.5 system, which performed extremely well by producing a low TYPE I error rate and no TYPE II errors in over 1000 trials. This technology has potential for the protection of C superscript 3 systems.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1986
Accession Number
ADA177714

Entities

People

  • Anthony M. Leigh Jr.

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • C4I
  • Ground and Sea Platforms
  • Human Systems
  • Space
  • Weapons Technologies

DTIC Thesaurus Topics

  • Authentication
  • Blood Vessels
  • Command And Control
  • Computer Access Control
  • Computers
  • Control Systems
  • Cybersecurity
  • Databases
  • Entry Control Systems
  • Identification
  • Information Processing
  • Pattern Recognition
  • Physical Access Control
  • Recognition
  • Reliability
  • Security
  • Test And Evaluation

Readers

  • Database Systems and Applications
  • Systems Analysis and Design
  • Vision Science/Vision Psychology/Cognitive Neuroscience.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • Fully Networked C3
  • Fully Networked C3 - Command and Control