Liberating the Biometric Menagerie Through Score Normalization Improvements

Doctoral Dissertation


The 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.


Attribute NameValues
  • etd-07152013-100819

Author Jeffrey Richard Paone
Advisor Dr. Patrick Flynn
Contributor Dr. Jesus Izaguirre, Committee Member
Contributor Dr. Patrick Flynn, Committee Chair
Contributor Dr. Kevin Bowyer, Committee Member
Contributor Dr. Nitesh Chawla, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2013-06-19

Submission Date 2013-07-15
  • United States of America

  • biometric menagerie

  • facial recognition

  • biometrics

  • score normalization

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

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