Quality Metrics for Biometrics

Master's Thesis
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Abstract

The accuracy of a biometrics system increases with the quality of the sample used for identification. All current iris biometrics systems capture multiple samples that must be processed to identify a single, ideal image to be used for identification. Many metrics exist to evaluate the quality of an iris image. This thesis evaluates current metrics and introduces a method for determining the ideal iris image from a set of iris images by using the Mean Self-Match algorithm to examine the set of true matches. This proposed method is shown to outperform other methods currently used for selecting an ideal image from a set of iris images. The application of this method to face biometrics is also examined.

Attributes

Attribute NameValues
URN
  • etd-04122011-181224

Author James S Doyle
Advisor Dr. Patrick Flynn
Contributor Dr. Guarav Aggarwal, Committee Member
Contributor Dr. Patrick Flynn, Committee Chair
Contributor Dr. Kevin Bowyer, Committee Member
Degree Level Master's Thesis
Degree Discipline Computer Science and Engineering
Degree Name MSCSE
Defense Date
  • 2011-04-08

Submission Date 2011-04-12
Country
  • United States of America

Subject
  • mean self-match

  • face

  • quality

  • metric

  • iris

  • MSM

  • biometrics

  • recognition

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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