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Title:Appearance based modeling and learning of the human face with application to biometrics
Author(s):Xu, Xun
Director of Research:Huang, Thomas S.
Doctoral Committee Chair(s):Huang, Thomas S.
Doctoral Committee Member(s):Ahuja, Narendra; Levinson, Stephen E.; Liang, Zhi-Pei
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Degree:Ph.D.
Genre:Dissertation
Subject(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.
Issue Date:2010-06-29
URI:http://hdl.handle.net/2142/16531
Rights Information:Copyright 2010 Xun Xu
Date Available in IDEALS:2012-06-29
Date Deposited:May 2010


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