Three Dimensional Face Recognition of Identical Twins

Master's Thesis

Abstract

Two problems that are generally considered to be difficult in face recognition are variations in expression and distinguishing between the faces of identical twins. This work consists of a study on the performance of 3D face recognition algorithms on a relatively large set of identical twins: the 3D Twins Expression Challenge (“3D TEC’) dataset. The 3D TEC dataset is a challenging dataset that consists of 3D scans of 107 pairs of twins that were acquired in a single session, with each subject having a scan of a neutral expression and a smiling expression. The combination of factors related to the facial similarity of identical twins and the variation in facial expression makes this a challenging dataset. In this paper, we demonstrate the performance of modern face recognition algorithms on this dataset. The results indicate that 3D face recognition of identical twins in the presence of varying facial expressions is far from a solved problem. An approach based on dimensionality reduction of the geodesic distance matrix is also attempted against the dataset. It was found that the use of geodesic distances did not improve performance for a certain class of algorithms due to the reduction in resolution needed for calculating the geodesic distance matrix.

Attributes

Attribute NameValues
URN
  • etd-07172012-171411

Author Vipin Vijayan
Advisor Sidney DMello
Contributor Patrick J. Flynn, Committee Member
Contributor Sidney DMello, Committee Member
Contributor Kevin W. Bowyer, Committee Member
Degree Level Master's Thesis
Degree Discipline Computer Science and Engineering
Degree Name MSCSE
Defense Date
  • 2012-05-07

Submission Date 2012-07-17
Country
  • United States of America

Subject
  • 3d face recognition

  • computer vision

  • identical twins

  • biometrics

Publisher
  • University of Notre Dame

Language
  • English

Record Visibility and Access Public
Content License
  • All rights reserved

Departments and Units

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