PaoneJR072013D.pdf (21.22 MB)
Liberating the Biometric Menagerie Through Score Normalization Improvements
thesis
posted on 2013-07-15, 00:00 authored by Jeffrey Richard PaoneThe biometric menagerie, or biometric zoo, is a classification system used to label the matching tendencies of a given subject?s biometric signature. These tendencies may include matching their own signatures poorly or matching other subjects? signa- tures better than their own. Several experiments show the biometric menagerie to be an unstable classification system where subjects frequently change class labels. In an attempt to improve the stability of the biometric menagerie, existing score normal- ization techniques are expanded to create Covariate F-Normalization (CovF-Norm). When the normalization methods are applied to the biometric menagerie, the classifi- cation system remains unstable and unreliable for practical use with subject-specific thresholding. The new normalization method, CovF-Norm, is also shown to be algo- rithm independent and data set independent unlike the biometric menagerie which is dependent on both the algorithm and data set. CovF-Norm is shown to significantly improve performance when compared to the standard F-Normalization technique?s equal error rate.
History
Date Modified
2017-06-05Defense Date
2013-06-19Research Director(s)
Dr. Patrick FlynnCommittee Members
Dr. Jesus Izaguirre Dr. Kevin Bowyer Dr. Nitesh ChawlaDegree
- Doctor of Philosophy
Degree Level
- Doctoral Dissertation
Language
- English
Alternate Identifier
etd-07152013-100819Publisher
University of Notre DameProgram Name
- Computer Science and Engineering
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