Exploration of the Impostor Distribution for Face-Based Biometrics

Doctoral Dissertation

Abstract

Face-based biometrics has become popular in many security applications due to the compromises made between reliability, social acceptance, and privacy. Many high performance face biometric systems now exist which rely on machine learnable features. However, when errors occur these features are hard to decipher in order to adjust for these errors. Through this work a method is presented which identifies nonmatching errors - potential false accepts. Covariates, or metadata values, are then examined with respect to how they interact with score outcomes and influence the likelihood of a false accept occurring. Last, an attempt is made to identify a connection between geometric and color features and score outcomes. Overall, this dissertation shows that the impostor distribution and nonmatching image pairs hold a plethora of information that can be used to improve biometric algorithms and systems.

Attributes

Attribute NameValues
URN
  • etd-04122015-162128

Author Amanda Jean Sgroi
Advisor Dr. Patrick Flynn
Contributor Dr. Patrick Flynn, Committee Co-Chair
Contributor Dr. P. Jonathon Phillips, Committee Member
Contributor Dr. Sidney DMello, Committee Member
Contributor Dr. Kevin Bowyer, Committee Co-Chair
Contributor Dr. Oleg Komogortsev, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name PhD
Defense Date
  • 2015-03-25

Submission Date 2015-04-12
Country
  • United States of America

Subject
  • score analysis

  • biometrics

  • computer vision

  • covariates

  • image morphing

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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