Exploring the Structure of the Similarity Score Space

Master's Thesis

Abstract

Similarity scores in face recognition represent the proximity between pairs of images as computed by a matching algorithm. Given a large set of images and the proximities between all pairs, a similarity score space is defined. Cluster analysis was applied to the similarity score space to develop various taxonomies. Given the number of subjects in the dataset, we used hierarchical methods to aggregate images of the same subject. We also explored the hierarchies occurring above and below this level, including clustering by gender and ethnicity. Furthermore, image specific error rates (a tool for measuring the usefulness of iris images within a dataset) were extended to face images. Observations on the effectiveness of this adaption are presented.

Attributes

Attribute NameValues
URN
  • etd-04182013-141725

Author Jason M. Grant
Advisor Patrick J. Flynn
Contributor Laurel Riek, Committee Member
Contributor Ryan Lichtenwalter, Committee Member
Contributor Patrick J. Flynn, Committee Chair
Contributor Kevin W. Bowyer, Committee Member
Degree Level Master's Thesis
Degree Discipline Computer Science and Engineering
Degree Name MSCSE
Defense Date
  • 2013-04-05

Submission Date 2013-04-18
Country
  • United States of America

Subject
  • similarity score analysis

  • clustering

  • face recognition

  • biometrics

  • computer vision

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

Files

Please Note: You may encounter a delay before a download begins. Large or infrequently accessed files can take several minutes to retrieve from our archival storage system.