University of Illinois Urbana-Champaign

Appearance based modeling and learning of the human face with application to biometrics

Xu, Xun

Content Files
1_Xu_Xun.pdf

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https://hdl.handle.net/2142/16531

Description

Title
Appearance based modeling and learning of the human face with application to biometrics
Author(s)
Xu, Xun
Issue Date
2010-06-29T00:59:54Z
Director of Research (if dissertation) or Advisor (if thesis)
Huang, Thomas S.
Doctoral Committee Chair(s)
Huang, Thomas S.
Committee Member(s)
  • Ahuja, Narendra
  • Levinson, Stephen E.
  • Liang, Zhi-Pei
Department of Study
Electrical & Computer Eng
Discipline
Electrical & Computer Engr
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Date of Ingest
2010-06-29T00:59:54Z
Keyword(s)
  • computer vision
  • pattern recognition
  • machine learning
  • face recognition
  • face alignment
  • biometrics
Abstract
In this dissertation we study key problems in face processing, with a focus on the applications in biometrics, including both hard biometrics (i.e. conventional face recognition) and soft biometrics, where demographical attributes (e.g. gender and age) are recognized. We categorize face processing techniques into two classes: appearance-based approaches that model facial appearance holistically, and feature-based approaches, which rely on the localization of facial feature points. In this dissertation we argue that appearance-based approaches are well suited for various face processing tasks. A fully automatic face processing system consists of several major modules: detection, alignment and recognition. In this dissertation we study the modeling and learning issues in the face alignment and recognition stages, and propose a series of algorithms to tackle these problems, demonstrating how recent developments in machine learning and applied mathematics can be employed to create effective solutions in building fully automatic, appearance-based face processing systems. Although face detection is not covered, as it is a relatively well-solved problem, some algorithms proposed in this dissertation can potentially be applied to that problem as well.
Graduation Semester
2010-5
Permalink
http://hdl.handle.net/2142/16531
Copyright and License Information
Copyright 2010 Xun Xu

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