Multi-Biometric Approaches to Ear Biometrics and Soft Biometrics

Doctoral Dissertation


In this work, we explore hard and soft biometric systems. Hard biometrics are features that are used to uniquely identify individuals over time, while soft biometrics do not uniquely identify individuals and may not persist in the same state over an extended time.

We develop methods that enable recognition using 2D ear images. This recognition is performed using a dataset which contains various lighting and pose conditions, as well as time lapse. We explore the growing field of ensemble biometrics, which subdivide a biometric feature into parts, and combine the results of several parts to yield recognition results. We vary the number of parts, the size of each part, and the method used to build each ensemble and report recognition improvements. We also allow the parts to change shape both before and during training, which further improves performance.

We perform recognition using soft biometric features extracted from video. Although these features are not as reliable as traditional biometric features, they can still contribute to the recognition process. We find that using clothing color and height yield modest performance results that can be extended on their own or applied to other biometric systems.


Attribute NameValues
  • etd-11062009-203812

Author Christopher Dennis Middendorff
Advisor Kevin W. Bowyer
Contributor Nitesh Chawla, Committee Member
Contributor Amitabh Chaudhary, Committee Member
Contributor Kevin W. Bowyer, Committee Chair
Contributor Patrick J. Flynn, Committee Member
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name Doctor of Philosophy
Defense Date
  • 2009-09-11

Submission Date 2009-11-06
  • United States of America

  • recognition

  • ear

  • computer vision

  • biometrics

  • automated

  • surveillance

  • University of Notre Dame

  • English

Record Visibility Public
Content License
  • All rights reserved

Departments and Units

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