人脸表情识别文献
JournalofDonghuaUniversity（Eng．Ed．）Vol．29，No．1（2012）
71
FacialExpressionRecognitionBasedontheQ-shiftDT-CWTandRotationInvariantLBP
CHENLei（陈
蕾）
*
SchoolofElectronics＆InformationEngineering，SoochowUniversity，Suzhou215021，China
Abstract：Inthispaper，anovelmethodbasedondual-treecomplex
wavelettransform（DT-CWT）androtationinvariantlocalbinary
pattern（LBP）forfacialexpressionrecognitionisproposed．Thequartersampleshift（Q-shift）DT-CWTcanprovideagroupdelay
of1/4ofasampleperiod，andsatisfytheusual2-bandfilterbank
constraintsofnoaliasingandperfectreconstruction．Toresolveilluminationvariationinexpressionverification，low-frequency
coefficientsproducedbyDT-CWTaresetzeroes，high-frequency
coefficientsareusedforreconstructingtheimage，andbasicLBPhistogramismappedonthereconstructedimagebymeansofhistogramspecification．LBPiscapableofencodingtextureandshapeinformationofthepreprocessedimages．Thehistogramgraphsbuiltfrommulti-scalerotationinvariantLBPsarecombinedtoserve
asfeatureforfurtherrecognition．Templatematchingisadoptedtoclassifyfacialexpressionsforitssimplicity．Theexperimentalresultsshowthattheproposedapproachhasgoodperformanceinefficiencyandaccuracy．
Keywords：facialexpressionrecognition；dual-treecomplexwavelet
transform（DT-CWT）；localbinarypattern（LBP）；histogram；
similaritymeasureCLCnumber：TP391Documentcode：AArticleID：1672-5220（2012）01-0071-05
，WANGJia-jun（王加俊），SUNBing（孙兵）
Introduction
Facialexpressionrecognitionistoanalyzeanddetectthespecialexpressionstatefromgivenexpressionimagesorvideoframesandthentoascertainthesubject'sspecificinbornemotion，achievingsmarterandmorenaturalinteractionbetweenhumanbeingsandcomputers．Inhumantohumaninteraction，Mehrabiandiscoveredthatverbalcuesprovided7%ofthemeaningofthemessage；vocalcues，38%；andfacialexpressions，55%［1］．Thusfacialexpressionprovidesmoreinformationabouttheinteractionthanthespokenwords．Automaticfacialexpressionrecognitionplaysanimportantroleinthedevelopmentofpatternrecognition，computervision，computergraphics，artificialintelligence，physiology，psychologyandsoon．Thestudyoffacialexpressionrecognitionhasfounditsvaluesineconomyandsociety．
Duetoitsapplicationsonsociologyandcomputervision，automaticfacialexpressionrecognitionhasattractedmoreandmoreattention．In1978，onthebasisoftheanatomy，EkmanandFriesenbuiltthefaceactioncodingsystem（FACS）thatassociatedfacialexpressionwithmusclemovement［2］．ByFACS，encodingallpossiblefaceexpressionsbecameareality．Thenin1984，EkmanandFriesenputforwardthatthecombinationofspecificFACSactionunitscouldindicatefacialexpressionofemotions［3］．Accordingtodifferentemotions，facialexpressionscanbedividedintosixtypes：happy，sad，surprise，fear，anger，anddisgust［4，5］．Thesixbasictypesareagreedwidelybyresearchersandtreatedasthefacialexpressioncategories．Facialexpressionrecognitiongenerallyincludesthreestages：facedetectionandlocalization，thefacialfeatureextraction，andexpressionrecognition．Facialexpressionisverycomplex，forexample，iftheopeningmouthdoesnotrepresentsmilenecessarily，itmaybecryorsurprise，andthesamekindofexpressionsmayhavealotofdifferentways，suchashappy，
someareopenmouthcachinnation，someareclosedmouthsmile．Therefore，thefacialexpressionanalysisisadifficulttaskthatmainlyreflectsintheaccuracyexpressionfeatureextractionandvalidityofexpressionfeatureextraction．Researchershavemadesomeachievementsinfacialexpressionanalysis．Therearethefollowingmethods：principalcomponentanalysis（PCA），independentcomponentanalysis（ICA），lineardiscriminantanalysis（LDA），fisherlinearjudgingmethod，clusteringdiscriminantanalysis，elasticchartmatchingmethod，gaborwaveletmethod，andlocalprincipalcomponentanalysis，etc．Everyalgorithmmentionedhassomeeffects，butisnotverysatisfactoryandneedstobeimproved．
Inthispaper，abriefreviewofDT-CWTandlocalbinary
patterns（LBPs）isintroducedandanovelmethodforfacialexpressionfeatureextractionisproposed．Bythismethod，theapproximationcoefficientsderivedfromDT-CWTareresetand
detailscoefficientsarepreserved．InverseofDT-DWTis
employedbyusingmodifiedcoefficients，andthereconstructedimageisreferredasIdt．ThebasicLBPhistogramoforiginalimageIismappedontoIdtbymeansofhistogramspecification，andtheresultedimageisdenotedasILBP．ThefusionimageofIdtandILBPisIpro．Multi-scalespecialdecompositionisapplied
toIpro．ThecombinationofeachscalerotationinvariantLBPhistogramsisusedasfeatureforrecognition．Experimentsshowthatthemethodpresentedinthispaperhashigherrecognitionrateandefficiency．
1
TheQuarterSampleShift（Q-shift）
DT-CWT
Thediscretewavelettransform（DWT）ismostcommonlyusedinitsmaximallydecimatedform．Thisworkswellforcompressionbutitsusesforothersignalanalysisandreconstructiontaskshavebeenhamperedbytwomaindisadvantages：lackofshiftinvarianceandpoordirectionalselectivity．InRefs．［6，7］，KingsburyintroducedanewformofDWT，whichgeneratedcomplexcoefficientsbyusingadual-treeofwaveletfilterstoobtaintheirrealandimaginaryparts．Thisintroduceslimitedredundancyandallowsthetransformtoprovideapproximateshiftinvarianceanddirectionallyselectivefilterswhilepreservingtheusualpropertiesofperfectreconstructionandcomputationalefficiencywithgoodwell-balancedfrequencyresponses．TheDT-CWThasreducedover
completenesscomparedwiththeshiftinvariantDWT（SIDWT），anincreaseddirectionalsensitivityovertheDWTthatisabletodistinguishbetweenpositiveandnegativeorientationsgivingsixdistinctsub-bandsateachlevel，theorientationsofwhichare
±15°，±45°，±75°．TheDT-CWTgivesperfect
reconstructionasthefiltersarechosenfromaperfectreconstructionbi-orthogonalset．TheQ-shiftDT-CWTisa
variantoftheearlierform，inordertogivethedual-tree
improvedorthogonalityandsymmetryproperties．TheQ-shift
versionoftheDT-CWTisshowninFig．1，inwhichallthe
filtersbeyondlevel1areevenlength，buttheyarenolongerstrictlylinearphase．Insteadtheyaredesignedtohaveagroup
Receiveddate：2011-09-28
*CorrespondenceshouldbeaddressedtoCHENLei，E-mail：chenlei@suda．edu．cn
delayofapproximately1/4sample（+q）．Therequireddelaydifferenceof1/2sample（2q）isthenachievedbyusingthetimereverseofthetreeafiltersintreebsothatthedelaybecomes3q．Furthermore，thefiltercoefficientsarenolongersymmetric，anditisnowpossibletodesigntheperfect-reconstructionfiltersetstobeorthonormal，sothatthereconstructionfiltersarejustthetimereverseoftheequivalentanalysisfiltersinbothtrees．Henceallfiltersbeyondlevel1arederivedfromthesameorthonormalprototype
set．
Fig．2
Q-shiftDT-CWTonafacialexpressionimage：（a）original
image；（b）realpart；（c）imaginarypart；（d）magnitude
2
2．1
RotationInvariantLBPsandFacialExpressionFeatureExtraction
LBPs
TheLBPoperatorwasfirstintroducedbyOjalaetal．［8］
andwasprovedapowerfulmeansoftexturedescription．Theoperatorlabelsthepixelsofanimagebythresholdinga3×3neighborhoodofeachpixelwiththecentervalueandconsideringtheresultsasabinarynumber（seeFig．3foranillustration）．Bydefinition，LBPoperatordiscardstheilluminationchanges，sinceitdependsonthegray-scale．This
makesitattractivesincedealingwithvaryingilluminationwhereinitisthemainconcerninour
research．
Fig．1
TheQ-shiftDT-CWT，givingrealandimaginarypartsof
complexcoefficientsfromtreeaandtreebrespectively（q=1/4sampleperiod）
NotethatfortheQ-shiftCWTeachcomplexwaveletbasis
iscenteredontheequivalentcomplexscalingfunctionbasis，andeachoftheseiscenteredbetweenapairofadjacentcomplexbasesfromtheprevious（finer）level．Inthisway，eachcomplexwaveletcoefficientatlevelkhastwocomplexchildrenlocatedsymmetricallyaboveitatlevelk－1．Fortheodd/evenDT-CWT，suchsymmetriesdonotoccur．Insummary，Q-shiftDT-CWThasthefollowing
properties：approximateshiftinvariance，gooddirectional
D）withgabor-likefilters（alsotrueselectivityin2-dimension（2-forhigherdimensionality，m-D），perfectreconstructionusing
Nshortlinear-phasefilters，limitedredundancy，efficientorder-computation，improvedorthogonalityandsymmetryproperties．
TheQ-shiftDT-CWTonafacialexpressionimageisshownin
Fig．
2．
Fig．3ThebasicLBPoperator（LBP=1+8+32+128=169）
LBPisnotrotationinvariant，whichisundesirableincertainapplications．ItispossibletodefinerotationinvariantversionsofLBP，andonesolutionisillustratedinFig．4，whereLBPROTrepresentsthevalueofonerotationinvariantpattern．Thebinaryvaluesofthethresholdedneighborhoodaremappedintoan8-bitwordinclockwiseorcounter-clockwiseorder．An
arbitrarynumberofbinaryshiftsisthenmade，untilthewordmatchesoneofthe36differentpatternsof“0”and“1”an8-bit
wordcanformunderrotation．Theindexofthematchingpatternisusedasthefeaturevalue，describingtherotationinvariantLBPofthisparticularneighborhood［9］
．
Fig．4Rotation-invariantversionofLBP
Thederivedbinarynumberscodifylocalprimitivesincludingdifferenttypesofcurvededges，spots，flatareas，etc．（asshowninFig．5），soeachLBPcodecanberegardedasamicro-pattern．ThelimitationofthebasicLBPoperatorisitssmall3×3neighborhoodwhichcannotcapturedominantfeatureswithlargescalestructures．Hencetheoperatorwaslaterextendedtouseneighborhoodofdifferentsizes［10］．Usingcircularneighborhoodsandbilinearlyinterpolatingthepixelvaluesallowanyradiusandnumberofpixelsintheneighborhood．Figure6illustratessomeexamplesoftheextendedLBPoperator，wherethenotation（P，R）denotesaneighborhoodofPequallyspacedsamplingpointsonacircleofradiusofRthatformacircularlysymmetricneighborset．
TheLBPoperatorLBP（P，R）produces2Pdifferentoutputvalues，correspondingtothe2PdifferentbinarypatternsthatcanbeformedbythePpixelsintheneighborset．Ithasbeenshownthatcertainpatternscontainmoreinformationthanothers．Therefore，itispossibletouseonlyasubsetofthe2PLBPstodescribethetextureofimages．Ojalaetal．［8］calledthesefundamentalpatternsasuniformpatterns．AnLBPiscalleduniformifitcontainsatmosttwobitwisetransitionsfrom0to1orviceversawhenthebinarystringisconsideredcircular．Forexample，00000000，001110000，and11100001areuniformpatterns．Itisobservedthatuniformpatternsaccountfornearly90%ofallpatternsinthe（8，1）neighborhoodandforabout70%inthe（16，2）neighborhoodintextureimages．Accumulatingthepatternswhichhavemorethan2transitions
2
intoasinglebinyieldsanLBPoperator，denotedLBPriu（P，R），withlessthan2Pbins．Superscriptriu2standsforrotationinvariantuniformLBPandlabelingallremainingpatternswithasinglelabel［11，12］．Forexample，thenumberoflabelsforaneighborhoodof8pixelsis256forthestandardLBPbut59for
riu2
LBP16，2．AfterlabelinganimagewiththeLBPoperator，ahistogramofthelabeledimagefl（x，y）canbedefinedasHi=
andhighfrequencycomponents（cDjs），withQ-shiftfilter．
（2）ZeroallthecoefficientsincAj，andpreservethecoefficientincDjs．EmployinverseofDT-CWTbyusing
modifiedcAjtogetherwithcDjs，referredasIdt．
（3）DerivethebasicLBPhistogramfromtheoriginalimageI．
（4）MapthebasicLBPhistogramofIontoIdt，bymeansofhistogramspecification．TheresultedimageisdenotedasILBP．
（5）ConvertbothIdtandILBPintofrequencydomainbyQ-shiftDT-CWTandthenfuseapproximationanddetails
coefficientsrespectively．AnimageIproisreconstructedbythefusedcoefficients．
（6）ThepreprocessedimageIproisdividedintomulti-level
sub-regions．TherotationinvariantLBPhistogramsarederivedfromthesesub-regions，normalizeddependingontheregion
sizes，andweightedaccordingtotheregionlocation．Thesehistogramsbuiltfromsub-blockscaneffectivelydescribefacial
expressionmicro-patterns．Theyarecombinedandservedas
featurevectorsforrecognition．
Figure7showslowandhighfrequencydirectionalcoefficientsofafacialexpression
image．
Fig．7
∑I（f（x，y）
x，y
l
=i），i=0，1，…，n－1，（1）
wherenisthenumberofdifferentlabelsproducedbytheLBP
operatorand
（2）I（A）=1，Aistrue，
0，Aisfalse．
Approximationanddetailscoefficients，withQ-shiftfilter：
（a）anoriginalimage；（b）approximationcomponent；（c）sixdirectionaldetailscomponents（magnitude）atlevel2；（d）detailscomponentsatlevel3
{
ThisLBPhistogramcontainsinformationaboutthedistributionofthelocalmicro-patterns，suchasedges，spotsand
flatareas，overthewholeimage，soitcanbeusedtostatisticallydescribeimagecharacteristics．
Aszeroingallthelowfrequencycoefficientsandpreservingthehighfrequencycoefficients，wecangetthereconstructedimageFig．8（b）byemployinginverseofDT-CWT．Figure
8（a）isthebasicLBPhistogramofFig．7（a），whichismappedontoFig．8（b），andFig．8（c）istheresultedimagebymeansofhistogramspecification．Figure8（d）isthefusionimageofFig．8（b）andFig．8（c）byapplyingDT-CWTfusion
method．
2．2Facialexpressionfeatrueextraction
Automaticfacialexpressionrecognitioninvolvestwovitalaspects：facialrepresentationandclassifierdesign．Facialrepresentationistoderiveasetoffeaturesfromoriginalfaceimagestoeffectivelyrepresentfaces．Theoptimalfeaturesshouldminimizewithin-classvariationsofexpressionswhile
maximizebetweenclassvariations．Ifinadequatefeaturesareused，eventhebestclassifiercouldfailtoachieveaccuraterecognition．InthispaperafeatureextractionalgorithmbasedontheDT-CWTandLBPhistogramsisproposed．Thewhole
processoffeatureextractionalgorithmiscarriedoutasfollows．
（1）Multi-levelDT-CWTisemployedtoconvertthegray-scalefacialimageIintotwocomponents，lowfrequency（cAj）
recognitionperformanceandfeaturevectorlength．Thusfaceimagesaretotallydividedinto32（1+2+4+9+16=32）regions．TheLBPfeaturesextractedfromeachsub-regionare
normalizedaccordingtothesub-regionsizesandthen
concatenatedintoasinglefeaturehistogramwiththelengthof416（32×26=416）．
3
Fig．8
Imagefusion：（a）thebasicLBPhistogramofFig．7（a）；（b）theimagereconstructedbyhighfrequencycoefficients；（c）resultedimagebymeansofhistogramspecification；（d）fusedimageforfeatureextraction
ExperimentalResults
Thecharacteristicvectorsareextractedusingthemulti-level
LBPhistogramsofthepreprocessedimageFig．8（d）．AnLBPhistogramcomputedoverthewholefaceimageencodesonlytheoccurrencesofthemicro-patternswithoutanyindicationabout
theirlocations．Toalsoconsidershapeinformationoffacialexpression，faceimagesareequallydividedintosmallregionsR0，R1，…，RmtoextractLBPhistograms（asshowninFig．9（a））．TheLBPfeaturesextractedfromeachsub-region
are
Forexperimentsweusedimagesfromoneofthepopulardatabasesforfacialexpressionrecognition，theJAFFEdatabase．TheJAFFEdatabasecontains213imagesofsevenfacialexpressions（6basicfacialexpressionsand1neutral）posedby10Japanesefemalemodels．Foreachwoman，thereare2-4imagesofeveryfacialexpression．EachimageisaTIFFimagewithsize256×256and256graylevels．Alltheimagesaretakenagainstahomogeneousbackgroundwiththesubjectsinfrontalposition．Someimagesof6basicfacialexpressions（anger，disgust，fear，joy，sadness，andsurprise）areshowninFig．10andcorrespondingimagesprocessedbyQ-shiftDT-CWT
inFig．
11．
Fig．9
ThenormalizedLBPhistograms：（a）sub-regionsateachlevel，
riu2
（b）labeledimageofmicro-patterns，（c）LBP24，3normalized
histogramsof9sub-regions，（d）concatenatedhistogramserved
asfeatureforrecognition
concatenatedintoasingle，spatiallyenhancedfeaturehistogram．Theextractedfeaturehistogramrepresentsthelocaltextureandglobalshapeoffaceimages．Someparameterscanbeoptimizedforbetterfeatureextraction．OneistheLBPoperator，andtheotheristhenumberofregionsdivided．We
riu2
selecttheLBP24，3operator，bywhichwedefine26rotationinvariantmicropatterns，anddividethefaceimagesinto1，2，4，9，16regionsrespectively，givingagoodtrade-offbetween
Thecorrectlabelsofthetrainingsamplesarevery
importantforrecognition．Asseveralexpressionimagesaremarkedwronginthedatabase，wecorrectthembeforeourexperiment．Wedodifferentexperimentsusingdifferentcharacteristicsandtwomatchingmethodstoanalyzethefacerecognitionperformances．ThecomposedLBPhistogramsof32multi-scalesub-regionsareservedasfeaturesandthose
histogramsofsub-regionsinvolvingmouthoreyesaresetbigger
weight．Thiscanimprovetherecognitionaccuracyeffectively．Weadoptedtemplatematchingtoclassifyfacialexpressionsandemployedtwomethodsforsimilaritymeasure．
（1）Foreachexpressionofonesubject，wetestthreetimesinturnandtaketheaveragerecognitionrateasthefinalresult．Wetakeoneofthefacialexpressionimagesasatestsampleandtherestastrainingoneseverytime．Thereisnooverlapbetweenthetrainingandtestimages．WeemployEuropeandistancemeasurementforrecognition．
（2）Intraining，thehistogramsofexpressionimagesinagivenclassareaveragedtogenerateatemplateforthisclass．Anearest-neighborclassifierisusedasdissimilaritymetricfor
comparingatargetfacehistogramtothemodelhistogram．
ResultsobtainedfromthedifferentexperimentsarepresentedinTable1．Inthetablewecanseehowdifferentfeaturesandsimilaritymeasuresaffectrecognitionrate．
Table1
Recognitionratesobtained
Recognitionrate（6expressions）65%84．5%89%100%
Features/matchingmethod
LBPof16same-scalesub-regions/distance
betweentestingsampleandtrainingsamplesLBPof32multi-scalesub-regions/distance
betweentestingsampleandtrainingsamplesWeigtedLBPof32multi-scalesub-regions/
distancebetweentestandtrainingsamplesWeigtedLBPof32multi-scalesub-regions/
distancebetweentestsampleandclasscentre
weusedatemplatematchingtoclassifyfacialexpressionsforitssimplicity．ComparedtherecognitionresultsobtainedwithourfacialfeaturestothoseobtainedwithPCAandLDAapproaches（76．3%and69．5%forPCAandLDA，respectively），themethodproposedinthispaperclearlyshowedthebetterperformanceinrecognitionefficiencyandaccuracy．
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4Conclusions
InthispaperweanalyzedthebasicprincipleofDT-CWT
andLBPs．AnovelmethodbasedonQ-shiftDT-CWTand
rotationinvariantLBPwasproposedwhichwasefficientforrecognition．Q-shiftDT-CWTwasusedtoresolveillumination
variationinexpressionverification．RotationinvariantLBPswerecapableofdescribingtextureandshapeinformation．Toenhancethefacialrepresentation，wedividedthepreprocessedfacialimagesintoseveralsub-regionsofdifferentscales．The
LBPhistogramsofsub-regionswerenormalizeddependingon
theregionsizesandgaveadifferentweightdependingontheroleofthegivenregionsinrecognition．Forinstance，sincethemouthregionswereimportantforrecognition，ahighweightcouldbeattributedtothecorrespondingLBPhistograms．Thecombinedhistogramsthatcouldeffectivelydescribefacialexpressionmicro-patternswereservedasfeaturesfor
recognition．Ourmaingoalinthispaperwastoshowthehighdiscriminativepoweroftheproposedfacialfeatures．Therefore，