Face geometry and appearance modeling : concepts and applications

329 Pages·2011·6.154 MB·English

by  Zicheng Liu, Zhengyou Zhang

FaceGeometryandAppearanceModeling
Humanfacesarefamiliartoourvisualsystems.Weeasilyrecognizeaperson’sfacein
arbitrarylightingconditionsandinavarietyofposes,detectsmallappearancechanges,
andnoticesubtleexpressiondetails.Cancomputervisionsystemsprocessfaceimages
aswellashumanvisionsystemscan?
Faceimageprocessinghaspotentialapplicationsinsurveillance,imageandvideo
search, social networking, and other domains.Acomprehensive guide to this fasci-
nating topic, this book provides a systematic description of modeling face geometry
and appearance from images, including information on mathematical tools, physical
concepts, image processing and computer vision techniques, and concrete prototype
systems.
This book will be an excellent reference for researchers and graduate students in
computervision,computergraphics,andmultimediaaswellasapplicationdevelopers
whowouldliketogainabetterunderstandingofthestateoftheart.
dr. zicheng liuisaSeniorResearcheratMicrosoftResearch,Redmond.Hehas
workedonavarietyoftopicsincludingcombinatorialoptimization,linkedfigureani-
mation,andmicrophonearraysignalprocessing.Hiscurrentresearchinterestsinclude
activityrecognition,facemodelingandanimation,andmultimediacollaboration.Hehas
publishedmorethan70papersinpeer-reviewedinternationaljournalsandconferences
andholdsmorethan40grantedpatents.
dr. zhengyou zhang is a Principal Researcher with Microsoft Research,
Redmond,andmanagesthemultimodalcollaborationresearchteam.Hehaspublished
morethan200papersinrefereedinternationaljournalsandconferencesandcoauthored
3-D Dynamic Scene Analysis: A Stereo Based Approach (1992); Epipolar Geometry
inStereo,MotionandObjectRecognition(1996);ComputerVision(1998);andFace
DetectionandAdaptation(2010).HeisanIEEEFellow.
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511977169
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511977169
Face Geometry and Appearance Modeling
Concepts andApplications
ZICHENG LIU
MicrosoftResearch,Redmond
ZHENGYOU ZHANG
MicrosoftResearch,Redmond
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511977169
cambridge university press
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Firstpublished2011
PrintedintheUnitedStatesofAmerica
AcatalogrecordforthispublicationisavailablefromtheBritishLibrary.
LibraryofCongressCataloginginPublicationData
Liu,Zicheng,1965–
Facegeometryandappearancemodeling:conceptsandapplications/ZichengLiu,
ZhengyouZhang.
p. cm.
Includesbibliographicalreferencesandindex.
ISBN978-0-521-89841-6(hardback)
1. Computervision. 2. Computergraphics. 3. Imageprocessing. I. Zhang,
Zhengyou,1965– II. Title.
TA1634.L582011
006.4(cid:1)2–dc22 2011002438
ISBN978-0-521-89841-6Hardback
CambridgeUniversityPresshasnoresponsibilityforthepersistenceoraccuracyofURLs
for external or third-party Internet Web sites referred to in this publication and does not
guarantee that any content on such Web sites is, or will remain, accurate or appropriate.
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Contents
1 Introduction page1
1.1 Literaturereview 2
1.2 Scopeofthebook 11
1.3 Notation 13
PARTI FACEREPRESENTATIONS
2 Shapemodels 19
2.1 Mesh 19
2.2 Parametricsurfaces 20
2.3 Linearspacerepresentation 21
2.4 Expressionspacerepresentation 25
3 Appearancemodels 31
3.1 Illuminationmodels 31
3.2 Irradianceenvironmentmap 36
3.3 Sphericalharmonics 36
3.4 Morphablemodeloffacealbedo 39
PARTII FACEMODELING
4 Shapemodelingwithactivesensors 43
4.1 Laserscanners 43
4.2 Structuredlightsystems 46
4.3 Structuredlightstereosystems 47
5 Shapemodelingfromimages 48
5.1 Structurefrommotionapproach 48
5.2 Stereovisionapproach 77
v
vi Contents
5.3 Twoorthogonalviews 80
5.4 Asingleview 82
6 Appearancemodeling 86
6.1 Reflectometry 86
6.2 Reconstructionofirradianceenvironmentmaps 91
6.3 Illuminationrecoveryfromspecularreflection 101
7 Jointshapeandappearancemodeling 111
7.1 Shapefromshading 111
7.2 Facemorphablemodel 113
7.3 Sphericalharmonicbasismorphablemodel 118
7.4 Data-drivenbilinearilluminationmodel 128
7.5 Spatiallyvaryingtexturemorphablemodel 129
7.6 ComparisonbetweenSHBMM-andMRF-basedmethods 140
PARTIII APPLICATIONS
8 Faceanimation 149
8.1 Talkinghead 149
8.2 Facialexpressionsynthesis 150
8.3 Physicallybasedfacialexpressionsynthesis 153
8.4 Morph-basedfacialexpressionsynthesis 153
8.5 Expressionmapping 154
8.6 Expressionsynthesisthroughfeaturemotionpropagation 166
9 Appearanceediting 181
9.1 Detailtransfer 181
9.2 Physiologicallybasedapproach 191
9.3 Virtuallighting 194
9.4 Activelighting 202
10 Model-basedtrackingandgazecorrection 217
10.1 Headposeestimation 218
10.2 Monocularheadposetracking 235
10.3 Stereoheadposetracking 236
10.4 Multicameraheadposetracking 242
10.5 Eye-gazecorrectionforvideoconferencing 251
11 Humancomputerinteraction 257
11.1 Conversationalagent 257
11.2 Face-basedhumaninteractiveproofsystem 268
Bibliography 279
Index 295
Colorplatesfollowpage146
http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511977169
1
Introduction
Faceimageprocessinghasbeenafascinatingtopicforresearchersincomputer
vision,computergraphics,andmultimedia.Humanfaceissointerestingtous
partly because of its familiarity. We look at many different faces every day.
Werelyonourfaceasacommunicationchanneltoconveyinformationthatis
difficultorimpossibletoconveyinothermeans.Humanfacesaresofamiliar
to our visual system that we can easily recognize a person’s face in arbitrary
lightingconditionsandposevariations,detectsmallappearancechanges,and
noticesubtleexpressiondetails.Acommonquestioninmanyresearchers’minds
iswhetheracomputervisionsystemcanprocessfaceimagesaswellasahuman
visionsystem.
Faceimageprocessinghasmanyapplications.Inhumancomputerinterac-
tion,wewouldlikethecomputertobeabletounderstandtheemotionalstate
oftheuserasitenrichesinteractivityandimprovestheproductivityoftheuser.
Incomputersurveillance,automaticfacerecognitionisextremelyhelpfulfor
boththepreventionandinvestigationofcriminalactivities.Inteleconferencing,
faceimageanalysisandsynthesistechniquesareusefulforvideocompression,
imagequalityenhancement,andbetterpresentationofremoteparticipants.In
entertainment,facemodelingandanimationtechniquesarecriticalforgenerat-
ingrealistic-lookingvirtualcharacters.Somevideogamesevenallowplayers
tocreatetheirpersonalizedfacemodelsandput“themselves”inthegame.
Thelastdecadehasbeenanexcitingperiodforfaceimageprocessingwith
many new techniques being developed and refined. But the literature is scat-
tered,andithasbecomeincreasinglydifficultforaperson,whoisnewinthis
field, to search for published papers and to get a good understanding of the
stateoftheart.Thegoalofthisbookistoprovideasystematictreatmentofthe
technologiesthathavebeendevelopedinthisfield.Wehopethatitwillserveas
agoodreferenceforpractitioners,researchers,andstudentswhoareinterested
inthisfield.
1
2 1 Introduction
Faceimageprocessingisabroadfield.Itisvirtuallyimpossibletocoverall
thetopicsinthisfieldinasinglebook.Thisbookmainlyfocusesongeometry
and appearance modeling, which involves the representation of face geom-
etry and reflectance property and how to recover them from images. Face
geometry and appearance modeling is a core component of the face image
processing field. In general, any face image processing task has to deal with
poseandlightingvariations,whichcanbeaddressedbyapplying3Dgeometry
andappearancemodelingtechniques.Someoftheseapplicationsaredescribed
inthelastpartofthebook.
Inthischapter,wegivealiteraturereviewanddescribethescopeofthebook
andthenotationscheme.
1.1 Literaturereview
Human perception of faces has been a long-standing problem in psychology
and neuroscience. Early works can be traced back to Duchenne [49, 50] and
Darwin[42].Duchenne[49,50]studiedtheconnectionbetweenanindividual
facial muscle contraction and the resulting facial movement by stimulating
facialmuscleswithelectrodestotheselectedpointsontheface.Darwin[42]
studied the connections between emotions and facial expressions. In 1888,
Galton [69] studied how to use iris and finger prints to identify people. In
thepastdecade,therehasbeenalotofstudyonwhatfeatureshumansuseto
recognizefacesandobjectsandhowthefeaturesareencodedbythenervous
system[17,56,70,89].
Earlyresearchoncomputationalmethodsforfacerecognitioncanbetraced
backtoBledsoe[20],Kelly[109],andKanade[106,107].Kanade’sthesis[106]
isapioneerworkonfacialimageanalysisandfeatureextraction.Intheseworks,
theclassificationwasusuallydonebyusingthedistancesbetweendetectedface
featurepoints.
Early1970swasalsothetimeframewhenthefirstcomputationalmethodfor
faceimagesynthesiswasdeveloped.In1972,Parke[162]developedapolygo-
nal head model and generated mouth and eye opening and closing motions.
After that, Parke [163] continued his research by collecting facial expres-
sionpolygondataandinterpolatingbetweenthepolygonalmodelstogenerate
continuousmotion.
In 1977, Ekman and Friesen [52] developed the Facial Action Encoding
System(FACS).ThisschemebreaksdownfacialmovementintoActionUnits
(AUs)whereeachAUrepresentsanatomicaction,whichisresultedfromthe
contractionofeitherasinglemuscleorasmallnumberofmuscles.FACShas
beenextensivelyusedforbothanalysisandsynthesistasks.
1.1 Literaturereview 3
In early 1980s, as part of the effort to develop a system for performing
the actions ofAmerican Sign Language, Platt and Badler [170] developed a
physically based representation for facial animation. In their representation,
faceskindeformationwascausedbythecontractionofmuscles,andamech-
anism was developed to propagate muscle contractions to the skin surface
deformation.
In1985,BergeronandLachapelle[13]producedthefamousanimatedshort
filmTonydePeltrie.Theyphotographedarealpersonperforming28different
facialexpressions.Theseimagesweremarkedmanually,andthefeaturepoint
motions were used to drive their face model. Their work demonstrated the
poweroffacialexpressionmapping.
In1986and1987,Pearceetal.[165]andLewisandParke[124]developed
speech-driven animation systems that used phoneme recognition to drive lip
movement.In1987,Waters[233]developedamusclemodelforgenerating3D
facialexpressions.ComparedtotheworkofPlattandBadler[170],Waters’s
modelismoreflexibleinthatitavoidshard-codingthemappingfromamuscle
contraction to the skin surface deformation. This technique was adopted by
PixarintheanimatedshortfilmTinToy[115].
In1988,Magnenat-Thalmannetal.[140]proposedtorepresentafaceani-
mation as a series of abstract muscle action procedures, where each abstract
muscleactiondoesnotnecessarilycorrespondtoanactualmuscle.
The 1990s witnessed an exciting period of rapid advancement in both
face image analysis and synthesis fields. In 1990, range scanners such as
the ones manufactured by Cyberware, Inc., became commercially available.
Such scanners were extremely valuable for data collection. In the same year,
Williams [239] developed a performance-driven facial animation system. He
obtainedarealistic3DfacemodelbyusingaCyberwarescanner.Fortracking
purposes,aretroreflectivematerialwasputontheliveperformer’sfacesothat
asetofbrightspotscanbeeasilydetectedontheface.Themotionsofthespots
werethenmappedtotheverticesofthefacemodel.
TerzopoulosandWaters[208,209,210]developedaphysicallybasedmodel
tonotonlysynthesizebutalsoanalyzefacialexpressions.Theyusedatechnique
calledsnakes[108]totrackfacialfeaturecontours,andestimatedthemuscle
control parameters from the tracked contours. The recovered muscle control
parameterscanthenbeusedtoanimateasyntheticfacemodel.Leeetal.[121,
122] developed a system for cleaning up laser-scanned data, registering, and
insertingcontractilemuscles.
Since 1990, there has been a tremendous amount of work on image-based
face modeling and animation. In the following, we review the literature
alongfivedifferentbutinterconnectedthreads:statisticalmodelsandsubspace
4 1 Introduction
representation,geometrymodeling,appearancemodeling,animation,andthe
modelingofeyesandhair.
1.1.1 Statisticalmodelsandsubspacerepresentation
In 1987, Sirovich and Kirby [112, 199] proposed to use eigenvectors (called
eigenpictures)torepresenthumanfaces.In1991intheirseminarpaper[216],
TurkandPentlandproposedtousethisrepresentationforfacerecognition,and
they call the eigenvectors “eigenfaces.”Their work not only generated much
excitement in face recognition but also made Principal ComponentAnalysis
anextremelypopulartoolindesigningstatisticmodelsforimageanalysisand
synthesis.In1992,Yuilleetal.[251]developedatemplate-basedtechniquefor
detectingfacialfeatures.Theirtechniquecanbethoughtofasanextensionto
the snakes technique [108] in that the local elastic models in the snakes are
replacedbytheglobaldeformabletemplates.In1995,Cootesetal.[38]pro-
posedtouseastatisticshapemodel,calledActiveShapeModel(ASM),asa
betterdeformabletemplate.Anactiveshapemodelisconstructedbyapplying
PrincipalComponentAnalysistothegeometryvectorsofasetoffaceimages.
ASMwaslaterextendedtotheActiveAppearanceModel(AAM)[37],which
consistsofastatisticshapemodelaswellasastatistictexturemodel.In1997,
VetterandPoggio[220]introducedlinearobjectclassesforsynthesizingnovel
views of objects and faces. In 1999, Blanz and Vetter [19] further extended
thisideaandintroducedamorphablemodelfor3Dfacereconstructionfroma
singleimage.TheyusedtheUniversityofSouthFlorida(USF)datasetof200
Cyberware scanned faces to construct a statistic shape model and a statistic
texturemodel.Givenaninputfaceimage,theshapeandtexturemodelcoef-
ficients can be recovered through an analysis-by-synthesis framework. Since
then, both the USF dataset and the notion of the linear object classes have
becomeverypopular.LinearspacerepresentationsaredescribedinChapters2
and3.Theface-modelingtechniqueofBlanzandVetter[19]willbedescribed
inSection7.2.
The study of subspace representation of face images under varying illu-
minations also started to attract much attention in the same period. In 1992,
Shashua [194] proposed that, without considering shadows, the images of a
Lambertiansurfaceinafixedposeundervaryingilluminationsliveinathree-
dimensional linear space. In 1994, Hallinan [82] made a similar observation
and proposed to use a low-dimensional representation for image synthesis.
In 1995, Murase and Nayar [153] proposed to use low-dimensional appear-
ance manifolds for object recognition under varying illumination conditions.