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Unconstrained face landmark localization

Descriptive

TitleInfo
Title
Unconstrained face landmark localization
SubTitle
algorithms and applications
Name (type = personal)
NamePart (type = family)
Yu
NamePart (type = given)
Xiang
NamePart (type = date)
1986-
DisplayForm
Xiang Yu
Role
RoleTerm (authority = RULIB)
author
Name (type = personal)
NamePart (type = family)
Metaxas
NamePart (type = given)
Dimitris
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Dimitris Metaxas
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Advisory Committee
Role
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chair
Name (type = personal)
NamePart (type = family)
Elgammal
NamePart (type = given)
Ahmed
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Ahmed Elgammal
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Bekris
NamePart (type = given)
Kostas
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Kostas Bekris
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Advisory Committee
Role
RoleTerm (authority = RULIB)
internal member
Name (type = personal)
NamePart (type = family)
Badler
NamePart (type = given)
Norman
DisplayForm
Norman Badler
Affiliation
Advisory Committee
Role
RoleTerm (authority = RULIB)
outside member
Name (type = corporate)
NamePart
Rutgers University
Role
RoleTerm (authority = RULIB)
degree grantor
Name (type = corporate)
NamePart
Graduate School - New Brunswick
Role
RoleTerm (authority = RULIB)
school
TypeOfResource
Text
Genre (authority = marcgt)
theses
OriginInfo
DateCreated (encoding = w3cdtf); (qualifier = exact)
2015
DateOther (qualifier = exact); (type = degree)
2015-10
CopyrightDate (encoding = w3cdtf); (qualifier = exact)
2015
Place
PlaceTerm (type = code)
xx
Language
LanguageTerm (authority = ISO639-2b); (type = code)
eng
Abstract (type = abstract)
Nowadays, facial landmark localization in unconstrained environments has attracted increasing attention in computer vision, which is a fundamental step in face recognition, expression recognition, face tracking, editing, face animation, etc. We firstly introduce the problem of facial landmark localization and its relevant canonical and state-of-the-art techniques. Among the existed methods, when facilitating to the facial images under unconstrained environments, they may encounter problems from the large pose variation, partial occlusion, unpredictable illumination, etc. We then separately investigate each of the pose variation and partial occlusion problems. To overcome the shape variation caused by the pose changes, we propose an optimized part mixture model to fast search in the pose manifold and a bi-stage cascaded deformable shape model to refine the local shape variance. For partial occlusion, we propose a consensus of occlusion-specific regressors framework, which resists from the occlusion due to the large amount of regressors and the particularly designed occlusion patterns. Further, we aim at building a unified framework to jointly deal with the pose and occlusion problems. A pose-conditioned hierarchical part based regression method is designed to condition the pose into several pre-defined subspaces and localize the key positions in a hierarchical way, in which the occlusion is detected by the part regressors and further propagated through the hierarchical structure. The proposed facial landmark localization methods have shown more promising performance than those state-of-the-arts in both accuracy and efficiency, compared on both lab-environmental databases and multiple challenging faces-in-the-wild databases. Our face alignment methods are further applied to some human-computer interaction (HCI) applications, i.e. user-defined expression recognition and face and gesture based visual deception detection. The improved results from the applications further validate the advantages of our method under all kinds of uncontrolled conditions.
Subject (authority = RUETD)
Topic
Computer Science
RelatedItem (type = host)
TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier (type = RULIB)
ETD
Identifier
ETD_6715
PhysicalDescription
Form (authority = gmd)
electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
1 online resource (xxii, 152 p. : ill.)
Note (type = degree)
Ph.D.
Note (type = bibliography)
Includes bibliographical references
Subject (authority = ETD-LCSH)
Topic
Computer vision
Subject (authority = ETD-LCSH)
Topic
Face perception
Note (type = statement of responsibility)
by Xiang Yu
RelatedItem (type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier (type = local)
rucore19991600001
Location
PhysicalLocation (authority = marcorg); (displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier (type = doi)
doi:10.7282/T30Z7582
Genre (authority = ExL-Esploro)
ETD doctoral
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Rights

RightsDeclaration (ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder (type = personal)
Name
FamilyName
Yu
GivenName
Xiang
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime (encoding = w3cdtf); (qualifier = exact); (point = start)
2015-09-08 15:42:03
AssociatedEntity
Name
Xiang Yu
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical

RULTechMD (ID = TECHNICAL1)
ContentModel
ETD
OperatingSystem (VERSION = 5.1)
windows xp
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