SgroiAJ042015D.pdf (23.14 MB)
Exploration of the Impostor Distribution for Face-Based Biometrics
thesis
posted on 2015-04-12, 00:00 authored by Amanda Jean SgroiFace-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.
History
Date Modified
2017-06-05Defense Date
2015-03-25Research Director(s)
Dr. Patrick FlynnCommittee Members
Dr. P. Jonathon Phillips Dr. Sidney DMello Dr. Oleg KomogortsevDegree
- Doctor of Philosophy
Degree Level
- Doctoral Dissertation
Language
- English
Alternate Identifier
etd-04122015-162128Publisher
University of Notre DameProgram Name
- Computer Science and Engineering
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