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A face tracking system for dynamic event recognition
Descriptive
TitleInfo
Title
A face tracking system for dynamic event recognition
SubTitle
application to continuous recognition of non-manual markers of American sign language and to deception detection by kinesic analysis
Name
(type = personal)
NamePart
(type = family)
Michael
NamePart
(type = given)
Nicholas
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Nicholas Michael
Role
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(authority = RULIB)
author
Name
(type = personal)
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(type = family)
Metaxas
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(type = given)
Dimitris N
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Dimitris N Metaxas
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Advisory Committee
Role
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chair
Name
(type = personal)
NamePart
(type = family)
Pavlovic
NamePart
(type = given)
Vladimir
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Vladimir Pavlovic
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Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Eliassi-Rad
NamePart
(type = given)
Tina
DisplayForm
Tina Eliassi-Rad
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Stamos
NamePart
(type = given)
Ioannis
DisplayForm
Ioannis Stamos
Affiliation
Advisory Committee
Role
RoleTerm
(authority = RULIB)
outside member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
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Text
Genre
(authority = marcgt)
theses
OriginInfo
DateCreated
(qualifier = exact)
2012
DateOther
(qualifier = exact);
(type = degree)
2012-01
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Face tracking has numerous applications in the field of Human Computer Interaction and behavior understanding in general. Yet, face tracking is a difficult problem because the tracker must generalize to new faces, adapt to changing illumination, keep up with fast motions and pose changes, and tolerate target occlusion. We first present our efforts to develop a system for probabilistic face tracking, using anthropometric and appearance constraints. We then move onto the focus of our work, which is the application of the face tracker to two interesting recognition problems. Firstly, given that sign language is used as a primary means of communication by deaf individuals and as augmentative communication by hearing individuals with a variety of disabilities, the development of robust, real-time sign language recognition technologies would be a major step forward in making computers equally accessible to everyone. However, most research in the field of sign language recognition has focused on the manual component of signs, despite the fact that there is critical grammatical information expressed through facial expressions and head gestures. Therefore, we present our novel framework for robust tracking and analysis of facial expressions and head gestures, by means of a dynamic feature descriptor, a 3D face model and temporal models, with an application to sign language recognition. We apply it to successful continuous recognition of six different classes of non-manual grammatical expressions. Secondly, deception is present in our everyday social and professional lives and its detection can be beneficial, not only to us individually but to our society as a whole. For example, accurate deception detection can aid law enforcement officers in solving a crime. It can also help border control agents to detect potentially dangerous individuals during routine screening interviews. Therefore, we also present two novel methods for deception detection, using only visual cues extracted from our face tracker and a skin blob tracker, both with promising results. One is based on a novel kernel density descriptor of human behavior, which can differentiate normal behavior profiles from over-controlled and agitated ones, using nearest neighbor search. The other is based on the notion of subject-interviewer synchrony.
Subject
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Topic
Computer Science
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_3727
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
xvii, 124 p. : ill.
Note
(type = degree)
Ph.D.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = vita)
Includes vita
Note
(type = statement of responsibility)
by Nicholas Michael
Subject
(authority = ETD-LCSH)
Topic
Face perception
Subject
(authority = ETD-LCSH)
Topic
Human-computer interaction
Subject
(authority = ETD-LCSH)
Topic
American Sign Language
Subject
(authority = ETD-LCSH)
Topic
Facial expression--Testing
Identifier
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http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064148
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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rucore19991600001
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NjNbRU
Identifier
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doi:10.7282/T3MC8Z1J
Genre
(authority = ExL-Esploro)
ETD doctoral
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Michael
GivenName
Nicholas
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2011-12-13 02:08:00
AssociatedEntity
Name
Nicholas Michael
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.
RightsEvent
DateTime
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(qualifier = exact);
(point = start)
2012-01-31
DateTime
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2014-01-30
Type
Embargo
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after January 30th, 2014.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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