Ear Biometrics in Human Identification

Doctoral Dissertation
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Abstract

Biometrics are physical or behavioral characteristics that can be used for human identification. Security plays an increasingly important role in our daily life, and biometric technologies are becoming the solution to highly secure recognition and verification of identity. In this dissertation, we propose the ear as a biometric and investigate its potential with both 2D and 3D data.

Our work is the largest experimental investigation of ear biometrics to date. The data set used for our experiments contains 415 persons, each with images acquired on at least two different dates. Approaches considered include a PCA (“eigen-ear”) approach with 2D intensity images and range images, a Hausdorff matching of edges from range images, and an ICP-based approach to matching the 3D data. Our experimental results demonstrate that the ICP-based approach outperforms the other approaches at a statistically significant level.

Furthermore, we develop the first fully automated biometric system using 3D ear shape. It is a complete automatic system starting with a raw 3D image, through automated segmentation of the ear, and 3D shape matching for recognition. The automatic system achieves a rank-one recognition rate of 97.6% on a 415-subject dataset. Our algorithm also shows good scalability of recognition rate with size of dataset. The results suggest a strong potential for 3D ear shape as a biometric.

In a biometrics scenario, gallery images are enrolled into the database ahead of the matching step, which provides the opportunity to build related information before the probe comes into the system. We present a novel approach, called “Pre-computed Voxel Closest Neighbors”, to reduce the computational time for shape matching in a biometrics context. The approach shifts the heavy computation burden to the enrollment stage, which can be done offline. Experiments in 3D ear and face biometrics demonstrate the effectiveness of the approach.

In addition, ear symmetry and partial ear shape experiments are investigated. The results indicate that most people’s left and right ears are approximately bilaterally symmetric. However, some people have ears with recognizably different shapes. Experimental results with partial ear shape suggest that minor hair covering does not affect the performance substantially, but large hair covering will certainly reduce the recognition rate. This suggests that even in circumstances where the complete ear shape cannot be captured, partial shape has potential for recognition. This lends support for using ear shape as a biometric.

Our experiments use the biometric database collected at the University of Notre Dame. This data set is available to other research groups.

Attributes

Attribute NameValues
URN
  • etd-07072006-101848

Author Ping Yan
Advisor Steven R. Schmid
Contributor Amitabh Chaudhary, Committee Member
Contributor Kevin W. Bowyer, Committee Member
Contributor Patrick J. Flynn, Committee Member
Contributor Gregory Madey, Committee Member
Contributor Steven R. Schmid, Committee Chair
Degree Level Doctoral Dissertation
Degree Discipline Computer Science and Engineering
Degree Name PhD
Defense Date
  • 2006-05-19

Submission Date 2006-07-07
Country
  • United States of America

Subject
  • biometrics

  • Image processing

  • ear biometrics

  • pattern recognition

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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