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1 (C)Intheproceedingsofthe``EuropeanConf.onMultimediaApplications, ServicesandTechniques-ECMAST;Louvain-la-Neuve,28-30May,1996'' Multi-modalpersonvericationtoolsusingspeech M.Acheroy RMA-B C.Beumier RMA-B andimages EPFL-CH G.Maitre B.Duc S.Fischer EPFL-CH D.Genoud IDIAP-CH EPFL-CH J.BigunyP.Lockwood MATRA-F IDIAP-CH G.Chollet IDIAP-CH S.Pigeon UCL-B L.Vandendorpe UCL-BUAT-GR I.Pitas K.Sobottka UAT-GR modalities.thevisualpartcurrentlyinvolvesi)matchingofacoarsegridcontaining Gaborphaseinformationfromfaceimages,ii)facialfeaturelocalizationandextraction iii)3dbiometricalfeatureextractionbystructuredlight.theacousticpartusesthree ableand,mostimportantly,constitutethetwomostimportanthumancommunication Weproposemulti-modalpersonvericationusingvoiceandimagesasasolutiontothe securedaccessproblem.thenecessaryi/odevicesarenowstandard,cheaplyavail- ABSTRACT methods(dtw,sosmandhmm)tocomparevoicereferencesextractedfromthe speechsignal.intheacousticpartlpccoecientsareextractedandthreedierent classiersareusedinparallel.theglobaldecisionistakenbyapplyingafuruithresholdtotheindividualmethodsandincombiningtheindividualresultsaccordingtoa majoritylaw. whichareinoperationorinplanningrelyheavilyonthetransfersofpin'soverthe unprotectedtelephonelines.thecreditcard(whicharearoundsinceseveraldecades) Intele-servicesandtele-shoppingapplications,theusagescenariosaresuchthatalarge numberofpotentialservicesarehamperedfrombeingputinoperation.manyofthose theftisanuisanceforindividualsaswellasforthemarketwhichacceptsthem.also 1.INTRODUCTION givenunlimiteduseofaservice,e.g.consultinganupdateddocumentordatabase,is withthecurrentpinbasedtechnologies,anapplicationinwhichaspecicuseris ThisworkhasbeencarriedoutwithintheframeworkoftheEuropeanACTS-M2VTSproject. yauthorforcorrespondance:epfl;de-lts;ch-1015;lausanne

2 diculttoopentowidepublicasthiswouldveryquicklylenditselftoabuseinthat someuserswouldvoluntarilygiveawaytheirpin.telebankingservicesbasedonvoice inaclosefutureasaturationinperformance.apromising,butchallengingwayof intermsofresources.cheapmono-modalrecognitiontechniquesarelikelytoreach andvideomessagingcouldeasilybeimplementediftheproblemofsecureverication availableonthemarketasproducts.theysuerhowever,fromvariousdrawbacks wasanintegralpartofthemessagingsystem. includinghighfalseacceptancerates,areperceivedinvasive,orsimplytoodemanding overcomingsuchlimitations,consistsincombiningresultsfromseveralmodalities. Theworkpresentedinthispaperisconcernedwithpersonidentication/verication Variousmonomodalpersonvericationmethodsareknownandarepartlyalready messagingmodalities,andarecheap,totheextentthattheyareoeredasstandard accessoriesofpersonalcomputersandworkstations. basedonfaceandvoicefeaturesextractedfromvisualandacousticdata.thechoiceof thesemodalities,[6],ismotivatedbythefactthatthesearepartofthenaturalhuman developmentoffusionmethods,i.e.themergeofindividualresultsgivenbyseparate thedirectanalysisofacombinationofdierentmodalities(e.g.studyofspeech/lip combiningsinglemodalities,inthiscontextfaceandvoicefeatures.thisrequiresthe analysisofdierentmodalities(e.g.voicetextureandfacefeatures)or,moreeciently, Thegoalofamulti-modalrecognitionschemeistoimprovetherecognitionratesby 2.DATABASEFORTESTS Theseshotsweretakenatoneweekintervalsorwhendrasticfacechangesoccurred ofextracting3-dfacefeaturesfromthesamedatabase. arethepresenceofsynchronizedspeechandimageinthedatabaseandthepossibility materialhadtoberecordedsincenoexistingdatabasecouldmeetourrequirementsof oeringallmodalitiesneededbythemultiplerecognitiontasks.theserequirements synchronization).duetotherelativenoveltyofmulti-modalidentication,ourown inthemeantime.duringeachshot,peopleareaskedtocountfrom'0'to'9'intheir nativelanguage(mostofthepeopleisfrenchspeaking),rotatetheheadfrom0to-90 theheadagainwithoutglassesiftheywearany.fromthewholesequence,3partsare degrees,againto0,thento+90andbackto0degrees.theyarealsoaskedtorotate extracted:the"voice"sequence,the"motion"sequenceandthe"glasseso"motion Ourcurrentdatabaseincludes37dierentfacesandprovides5shotsforeachperson. sequence(ifany).therstdatasequencecanbeusedforspeechverication,2-d dynamicfaceverication(choosingthemostappropriatepictureoutofthesequence) (headtilted,eyesclosed,dierenthairstyle,presenceofahat/scarf...),voicevariations andspeech/lipsmovementcorrelation.theothertwosequencesaremeantforface orshotimperfections(poorfocus,dierentzoomfactor,poorvoicesnr...). multipleviews.foreachpersonofthedatabase,themostdicultshottorecognizeis labeledasthe5thshot.theymainlydierfromtheothersbecauseoffacevariations otherrecognitiontechniqueslikerecognitionfrom2-dfacialpictures,proleviewand tothemotion.thesetwosequencesmayalsobeusedforimplementingandcomparing recognitionpurposesonlyandprovideinformationaboutthe3-dfacefeaturesthanks Itwasdecidedtousegoodqualitymaterialfortherecording,leavingspaceinthe

3 Hi8videocamera(576x720,50Hz-interlaced,4:2:2)waschosenfortheshootingand futuretodegradequalityinordertosimulateagivenlow-costacquisitionsystem.a ad1digitalrecorderfortherecordingandediting.inordertoreducethestorage requirement,televisionsequencesarethendown-convertedintocif(288x360pixels, 25Hz-Progressive,4:2:2).Thisconversionremoveseveryothereldandperformshorizontaldown-samplingintheremainingframewithrespecttotheMPEG-2TM5specication.Bykeepingactivepixelsonly,thenalresolutionforthedatabaseimages is286x350pixels.concerningvoiceacquisition,thesoundtrackisdigitallyrecorded given).nevertheless,wecannoticesomeimpairmentswithrespecttothetheoretical cooperativescenario(asmuchastheycould,peoplefollowedtheinstructionstheywere shooting,nearlyconstantillumination,uniformgraybackground)andwithinahighly havingbeenproducedunder"ideal"shootingconditions(goodpicturequality,indoor usinga48khzsamplingfrequencyand16bitlineardata. headinthedirectionoftherotation,verticaltiltdependingontherotationangle,no case:-somepeopledonorotatetheirheadproperly(horizontaltranslationofthe Besidestheparticularcaseofthelastshots,thedatabasecanbeconsideredas ofstartingtherotationoftheheadisnotxedoverthedierentshots,-somepeople duringonerotationofthehead,closedduringtheother,endingupondierentshapes intheproleview,-somepeopleclosetheireyeswhilemovingthehead,-thedirection fullcoveringofthe180frontaldegrees...),-somepeoplemighthavetheirmouthopen arespeakingverylow(resultinginapoorsoundsnr),-somepeoplecannotkeep duringfastheadrotation,duetolimitedshutterspeed. facedatabasecanbeseenasagoodmaterialtotesttherobustnessoftherecognitionalgorithmswithregardstocommonproblems.assuminganalgorithmwouldnot overcometheimperfectionsencounteredhere,itwouldbedicultforthisalgorithm plementingapracticalrecognitionscheme.moreoverpeoplewillexpecttherecognition However,similarimperfections(combinedwithotheraswell)willappearwhenim- fromsmilingduringtheshot,-rotationspeedcanbehighlyvariablebetweendierent algorithmstobeabletodealwithsuchimperfections.fromthispointofview,this shots,butalsowithinthesameshot,-reectionsoneyesandglasses,-blurryimages toovercomethoseassociatedwithtrueoperationalconditions. 3.1.Extractionoffacialfeatures Inthisapproachthebiometricfeaturesbasedonthedistancesbetweeneyesandmouth, aswellastheshapeoftheheadconstitutetherecognitionbase.duetovariationsin illumination,background,visualangleandfacialexpressions,theproblemiscomplex. 3.VERIFICATIONBYIMAGES Inthefollowingwepresentanapproachthatlocalizesfacesincolorimagesonthe searchingforfacialfeaturesinsideoftheface-likeregions.thisisdonebyapplying morphologicaloperationsandminimalocalizationtointensityimages. baseofshapeandcolor(hsv)information.thehypothesesforfacesareveriedby

4 Asdiscriminatingcolorinformationweconsidertheattributeshueandsaturation. Inourapproachwetakeadvantageoftheskin-speciccolorandtheovalshapeoffaces. offacialregionsoutofsceneswithcomplexbackgroundisaproblem. [12],shape[11]andcolorinformation[24]orcombinationsofthem.Stillthedetection Intheeldoffacelocalization,approacheshavebeenpublishedusingtexture[7],depth Theeectivenessofusingcolorinformationwasalsoshownin[7].Ourresultsof Facelocalizationandapproximation thesegmentationstepareshowninfig.1a,b. edges(e.g.[11]),butwehavechosenanapproachbasedonregions.theadvantage ofconsideringregionsisthattheyaremorerobustagainstnoiseandchangesinillumination.inarststepconnectedcomponentsaredeterminedbyapplyingaregion meanstodetectobjectswithnearlyellipticalshape.mostlythisisdonebasedon Becausefacesarecharacterizedbytheirovalshape,lookingforfacesinanimage (a) Figure1:Detectionoffacialregions (b) (c) V: componentcisapproximatedbye.thisisdonebyevaluatingthefollowingmeasure moments,wecomputethebest-tellipseeandthenweassess,howwelltheconnected eachconnectedcomponent,ifitsshapeisnearlyellipticalornot.forthat,basedon growingalgorithmatacoarseresolutionofthesegmentedimage.thenwecheckfor with countingthe"holes"insideoftheellipseandthepointsoftheconnectedcomponent Vdeterminesthedistancebetweentheconnectedcomponentandthebest-tellipseby V=P(x;y)2E(1?b(x;y))+P(x;y)2CnEb(x;y) b(x;y)=(1if(x;y)2c P(x;y)2E1 0otherwise (1) candidates.inthecaseoftheexampleweobtaintheresultsshowninfig.1c. thatareoutsideoftheellipse.basedonathresholdonthisratio,theellipsesthat Bysearchingforfacialfeaturesinsideoftheconnectedcomponents,thefacehypotheses aregoodapproximationsofconnectedcomponentsareselectedandconsideredasface areveried.

5 120 y_relief x_relief_eyes x_relief_mouth Inintensityimageseyesandmouthdierfromtherestofthefacebecauseofthecolor thisobservationandextractthepositionsofeyesandmouthbyanalysisofthetopographicgreylevelreliefinsideofface-likeregions. ofthepupils,thesunkeneye-socketandthelightredcolorofthelips.wemakeuseof Inapreprocessingstep,weenhancethedarkregionsbyapplyingagreyscaleerosion Eyesandmouthlocalization oftheconnectedcomponentandthendetermineminimainthisy-relief.beginning [19]andanextremumsharpeningoperation[18].Fortheexamplescene,theresults beardregionsareemphasized. Thepositionofeyesandmoutharedeterminedbysearchingforminimainthetopographicgrey-levelrelief.Forthatwerstcomputethemeangrey-levelofeveryrow therequirementsforeyesconcerninge.g.eyedistance,relativepositioninsideofhead, signicanceofmaximumbetweenthem.incasethatreliablycandidatesforeyesare found,welookformouthcandidates.thesearchisstartedbelowoftheeyes.again, ischosen. Examplesforsuchreliefsinx-andy-directionareshowninFig.2. rstthey-reliefisinvestigatedforminimaandincaseofaminimum,minimainxdirectionaredetected.foreachoftheseminimaischecked,ifitmeetstherequirements forthemouththatconcerne.g.widthofthemouth,relativepositioninsideofthehead illustratedinfigure2(left)areobtained.eyesandmouthandpartsofthehairand withtheuppermostminimainy-direction,wesearchforminimainx-direction.the positionsoftheeyesarefound,iftwominimainx-directionaredetectedthatmeet andrelativepositionbetweeneyesandmouth.onthisbasis,thebestmouthcandidate y-relief x-relief eyes Resultsofthedetectionoffacialfeaturesareshownintheright.Theeyesandthe moutharewelllocalized. x-directioncorrespondwiththeverticalpositionofeyesandmouth. Inthey-reliefminimacanbeseenforeyes,mouthandbeard.Theminimain Figure2:Detectionoffacialfeatures x-relief

6 intheimage.afeaturevectorcandescribecomplexstructureslikelinestops,corners, 3.2.Gaborresponsematchingonagridforfacerecognition Thefeaturevectorsweusearesetsoffeaturesthatdescribelocalpropertiesofpoints andcrossingswithouthavingamodeloranyaprioriknowledgeofthestructurebeing described. grid,similartothatin[17].comparingtwofaceimagesisaccomplishedbymatching Eachfaceisdescribedbyasetoffeaturevectorspositionedonnodesofacoarse Featurevectors andadaptingagridtakenfromoneimagetothefeaturesoftheotherimage. WeusecomplexGaborresponsestodeterminethefeaturevectorfromlterswith6 orientationsand3resolutions. Fig.3showsafeaturevectorthatisusedforeverygridnodeasfeaturevector. Scale 0 Orientations with6orientationsand3resolutions. Figure3:ThefeaturevectorswehaveusedarebasedonGaborfeaturesfromlters Scale 1 Themethodforgridmatchingweemployconsistsoftwosteps.Therst,coarsematchingtranslatestheundeformedgridthathasbeenstoredinadatabaseandattemptsto ndthebestcorrespondencebetweenthegridandtheimagetobeveried.thesecond Wehaveexaminedtwodierentfeaturevectors:phaseandmodulusofthecomplex MatchingGaborresponsesonagrid valuedvectorsweobtainedfromgaborresponses. Scale 2 stepperformslocalmatchesaroundthegridpointsinordertondlocalminima.this areliablemeasureforpatterndistance. gridandtestimage,andthesumofchangesineuclideandistancesbetweenthegrid nodescausedbythedeformation. dierentdistancemeasures,namelythesumofallfeaturevectordistancesbetween featurevectorscanbeusedasameasureforfacedierence.wehaveexaminedtwo resultsinadeformedgrid.thedegreeofdeformationaswellasthemismatchofthe Therstexperimentssuggestthatthesumofthefeaturevectordistancesprovides

7 faceimages. thegraphmatchingisonlyusedtotacklethecorrespondenceproblembetweentwo ofthedeformedrectanglesthatarebeingformedbytheedgesofthegrid.inthiscase PerformanceMeasures Othermethodsforcalculatingthegriddistancecouldbebasedonthegreylevels Anumberofmeasurescanbeusedtoevaluatetheperformanceofarecognitionscheme. Theperformancemeasuresthatareusedforpersonidenticationistherecognition improvementintherecognitionrate. usefulmeasurecouldbetheratiobetweenthecostofanadditionalmodalityandthe rate(rr),falserejectionrate(frr),andfalseacceptancerate(far).themultimodalapproachrequiresameasuretoevaluatetheimportanceofeverymodality.a havedevelopedinamulti-modalpersonidenticationscheme.hence,theneedofa personidenticationaredierent,andtherequirementsvaryconsiderably.inaddition forthecomparisonwithotheridenticationschemes.howevertheapplicationareasof specialfaceandspeechdatabasementionedpreviously. tothatweneedadatabasecontainingdierentmodalitiestoevaluatethemethodswe Theperformanceshouldbemeasuredonastandardfaceimagedatabasetoallow withasuperimposedtestgrid.fig.7showsthedeformedtestgridcomputedfrom Fig.6thatbestmatchestheimageinFig.5. Fig.5showtwofacestobecompared.Fig.6showsthesameimageasinFig.4but Forfacerecognition,wehaveusedanimagedatabasewith40persons.Fig.4and Resultsongridmatching image. Figure4:FirsttestFigure5:Second testimage. imagewithsuperimposedtestgrid.figure7:second Figure6:Firsttest recognisedinmostofthecasesasmoresimilarthanimagesfromdierentindividuals. pairsofimagesfrom10individualsnotdisplayinganyemotion,thathavebeentaken attimesmorethantwoweeksapart.wecanshowthatcorrespondingfaceimagesare Table1showsthedistancemeasurebetweenfaceimagesasamatrix.Wehaveused testimagewithdeformedtestgrid. Indeed,onecannoticethatforeverylineandeverycolumn,thediagonalelementisthe

8 smallest.thismeansthatbytakingtheminimumdistanceascriterionforrecognition, noerrorismade. personno entries)andofthesecondseries(horizontalentries). Table1:Thistableshowsthedistancesbetweentheimagesoftherstseries(vertical Fig.8illustratesgraphicallytheresults.Asmallerdistanceisdisplayedasadarker pixel. Distance Matrix takenatdierenttimeshavebeenused. Figure8:Thismatrixshowsthefacedistancesasgreylevels.Twoseriesof10images nitionmethodthatisnotrobusttolargescaling.thedistancefunctionforfeature ofthecomplexresponseasadistancemeasure.thisleadstolessreliablegridmatchingsthanwiththegaborphase.furthermore,gaborphaseproducesmoresmoothly featurevectorsintheimagehasonlyafewevanescentminimaifweusethemodulus deformedgridsthangabormagnitude,andwithoutadditionalsmoothnessconstraints. However,phaseascriterionforcomparingfeaturevectorsresultsinafacerecog- Thedistancefunctionthatdescribesthedistanceofonefeaturevectortoallother Second image series First image series

9 butalargescalingofthefaceintheimagetobetestedcancausethefeaturevectors rigidtranslationofthegridpreventsfeaturevectorstofallintowronglocalminima, vectorsbasedonphasehasalargenumberofwell-denedlocalminima.theinitial to\drop"intothefalseminima. mayalsobeusedasfeatures.especiallyifnorotationhastobeexpectedfromthe patternbeingsought,afastwavelettransformcouldbeusedtoacceleratethegrid matching. Otherfeatureslikelocalsymmetry,[4],localorientation,[3],ororthogonalwavelets, posture(orientation,scale).furthermoremuchstatic(robust),discriminantandeasy Weseestructuredlightasameanstogetridofproblemsrelatedtoilluminationand theinformationcontentsofthelabeledgrid[25]. 3.3.Structuredlight Animagecanbereconstructedpartlyfromthefeaturevectorgraphtodemonstrate isapossiblemeanstoincreaseprecisionorenlargethevolumeseen.theillumination surfaces(suchasaface)withonlyoneimage.consideringsequencesofsuchimages castingapatternoflines.thetechniqueallowstoreconstructsucientlywellbehaved 3Dmeasurementsarederivedfromtriangulationthankstoacameraandaprojector andbequickenoughtoavoidheadmotiondefectsandallowtimeintegration.the todetectcharacteristicslieinvolumeinformation(forehead,nose,cheek,lips,...). disturbance(propertoactive3dsystems)canbereducedbyoperatinginnearinfrared. Structuredlight,[15],shouldoerasucientprecision,remainsimpleandcheap, improvetheprecisionfurther.(seefigure9andfigure10).thecharacteristicsbrought provedpromising.wearecurrentlydevelopinganautomaticcalibrationprocedureto curvaturevalues,volumeanddistancebetweenheadpartswillbeextractedasfeatures. bythe3danalysiswillconcerncurvature,distanceandvolumeofspecicheadparts. First,theheadwillberoughlyanalyzedintermsofcurvature(convex,concave)to normalizethedatawithrespecttoorientationandscalevariations(see[13]).then Ourpreliminarytestswithano-the-shelfcameraandalowcostprojectorhave Thiswillbringnewinformationincomparisonwithgrey-levelbasedfeatures. Thesemethodsare:DynamicTimeWarping(DTW),Sphericity(SOSM),andHidden MarkovModels(HMM).Eachmethodoutputsascalarvalue,whichcanbeconsidered Thevericationbyspeechisbasedonthreemethodsforcomparingthespeechrecorded 4.1.Systemoverview forvericationwithareferencebuiltfromtrainingutterancesoftheclaimedperson. 4.VERIFICATIONBYSPEECH asameasureofconformityofthenewlyrecordedspeechwiththestoredspeakerreference.twoofthemethods(dtwandhmm)arespecictotext-dependentspeaker isveriedashesaidduringtraining.thethirdone(sosm)canbeusedforboth, hereinatext-dependentcase. verication,i.e.tothecasewherethepersonsaysthesametextwhenhisidentity text-dependentandtext-independent,speakerverication.allthemethodsareused

10 ThespeechparametersweusearetheLinearPredictionCepstralCoecients(LPCC) performedonspeechparameterswhichareextractedfromthecapturedaudiosignal. Thecomparisonofthespeechrecordedforvericationwiththespeakerreferenceis light Figure9:Imagewithstructured Figure10:3-Dreconstruction [1,23],theaimofwhichistomodelthevocaltractofthespeaker.Theyarecomputed every10ms,overatimewindowof25ms.thewaythespeakerreferenceisbuiltfrom thespeechparametersofthetrainingutterancesdependsonthecomparisonmethod (seesection4.2). istakenbasedontheresultsofthecomparisonmethods(conformitymeasures).in investigateddierentwaysofsettingthethreshold(seesection4.3).inthecasewhere thecomparisonresult,theproblembeingtondtheoptimalthresholdvalue.wehave onthecomparisonresults.thisisaproblemgoingintothetopicofmodalitiesfusion, thecaseofasinglecomparisonmethod,thedecisionissimplytakenbythresholding themethodsarecombined,therearemanypossibilitiesforachievingadecisionbased Thevericationdecision,i.e.thedecisionwhethertoacceptorrejecttheperson, whichisdiscussedinsection Individualspeechcomparisonmethods DynamicTimeWarping(DTW)Aspeakerreferencevectorisbuiltinaveraging oflpccsextractedfromthespeechrecordedforverication,wherebothspeechsequencesaresupposedtoexpressthesametext. LPCCoecientsextractedfromthetrainingspeech,andtestvector,thetimesequence vericationsystems.weexplainhereonlywhatisparticulartooursystem.forthis, weintroducethefollowingdenitions.wecalltrainingvector,thetimesequenceof DTW,SOSM,andHMMaremethodswhichhavealsobeenusedinotherspeaker thetrainingvectors(ofthesametext).thedynamiccomparisonperformedbythe

11 DTWmethodisappliedbetweenthetestandreferencevectors[14]. fromthecovariancematricesofthereferenceandtestvectors[9,5]. Sphericity(SOSM)Aspeakerreferencevectorisbuiltinconcatenatingthetraining HiddenMarkovModels(HMM)Weuseleft-rightHMMstructures[22].The statecontainsasinglegaussian. vectors(ofthesametext).theconformitymeasureisthesphericitymeasurecomputed numberofstatespermodelhasbeendeterminedempirically.thereisapproximatively onestateforeachphonemeandonestateforeachtransitionbetweenphonemes.each speakermodelisinitializedwiththeworldmodelandre-estimatedwiththetraining worldmodel.thetwomodeltypesofthesametextunithavethesamestructure. worldmodel)iscomputed. vectorsoftheconcernedspeaker. Alikelihoodratio(thelikelihoodofaspeakermodeldividedbythelikelihoodofthe Foreachtextunit(e.g.digit),thereexistsonemodelforeachspeakeranda 4.3.Acceptance/rejectiondecision Incaseofasinglecomparisonmethod(DTW,SOSM,orHMM),wehaveinvestigated Theworldmodeliscreatedfromadatabasewithalargenumberofspeakers.The results.however,ithastofacetheshort-comingofindividualtrainingdatatogivea goodestimateoftheoptimalthreshold.\eerglobal"isathresholdcommontoall orindividual(personal).thelattersolutionhasmoreexibilityandshouldgivebetter thepersons.\eerindividual"and\furui"areperson-dependentthresholds. [16]Ḃasically,thethresholdvaluecanbeeithergeneral(i.e.thesameforallthepersons) threewaysofsettingthedecisionthreshold:eerglobal,eerindividual,andfurui obtainedwiththe\furui"threshold,forthedtwandsosmmethods,andwiththe \EERglobal",fortheHMMmethod.Thislastresultcanbeexplainedbythefact that,inthehmmcaseandnotintheotherones,thecomparisonmeasurehasbeen normalizedbytakingtheratiobetweenthelikelihoodofthepersonmodelandthe likehoodoftheworldmodel. Onthedatabaseusedforexperimentation(seesection4.4),thebestresultswere achievingadecisionbasedonthecomparisonresults,wehaveusedthefollowingprocedure.individualdecisionsaretakeninapplyingathresholdontheindividualcomparisonresultsandtheglobaldecisionisrealizedinapplyingamajoritylawtothe individualresults.theperformanceachievedincombiningthecomparisonmethodsin thewayjustdescribedispresentedinthenextsection. Inthecaseofmultiplecomparisonmethods,amongthenumerouspossibilitiesfor 4.4.Results Theresultswhichareavailablenowconcernasetof10speakers,whosevoicedatais partofthepolycodedatabase[20].thisdatabasewasrecordedthroughatelephone lineinseveralsessions.ineachsession,eachspeakerhadtosay,amongothersentences 10digits,in4dierentorders(whicharethesameforallthespeakers). infrench,5timeshis7-digitspersonalidenticationnumber(pin)and4timesallthe

12 tests. PINnumberpronouncedbyhimselfand9x20samplesofhisPINnumberconstructed from10-digitssequencespronouncedbyeachoftheotherspeakers.so,intotal,there are200correctaccesstrialsand1800impostortrials. methodparameters,onefortrainingthespeakerreferences,andoneforverication Thedatausedforvericationareasfollows:foreachspeaker,20samplesofhis Thedatabasehasbeendividedintothreedisjointsetsofsessions:oneforestimating yeldsanimprovementofthefalserejectionrate.thefalseacceptanceisslighthly deteriorated.thiscanbeduetothesmallsizeofthedatabase,aswellasthecrudeness (ratioofimpostortrialsfalselyaccepted)andthefalserejectionrate(ratioofcorrect accesstrialsfalselyrejected). ofthefusionrule. Thevericationperformanceareexpressedintable2bytheFalseAcceptancerate Table2showsthatcombiningthemethodsinthewaydescribedinthelastsection Table2:Speech-basedvericationperformance(individualandcombinedmethods) Method DTW/Furuithreshold SOSM/Furuithreshold HMM/EERglobalthreshold CombinedDecision (200tests)(1800tests) FR% FA% developedsofarforfeaturesextractionandcomparisonaredierentfrommodalityto chain:data,features,ordecisionfusion[8].however,sincethemethodswehave modality,thefusionhastooccurinthelaststage,i.e.thefusionisconcernedwith modalitiescouldtakeplaceatanystepofthe\classical"patternrecognitionprocessing Therearemanypossiblewaysofcombiningmodalities.Inprinciple,thefusionof 5.FUSIONSTRATEGIES threevoicerecognitionmethods.theproblemofcombiningthecomparisonoutputs fortakingadecisionhasbeensolvedinapplyingtoeachindividualcomparisonresult comparisonmethods. aproperdecisionthresholdandincombiningtheindividualdecisionswithamajority law(seesection4.3). takingtheacceptance/rejectiondecisionbasedontheoutputgivenbythedierent Uptonow,fusionhasonlybeenstudiedandexperimentedwithrespecttothe toparticularconditionswhereamethodismoreorlesspowerful. gatingotherwaysofsolvingtheproblem,e.g.weareconsideringthepossibilityof investigatingthepossibilitiesofadaptingtheweightstoapersonoraperson-class,or weightingtheindividualdecisionoutputs.ratherthanusingconstantweights,weare Wearecurrentlyextendingthefusiontothevisualfeaturesandweareinvesti-

13 informationwithdynamiclinkmatchingisabletodiscriminatefaces.thestructured Concerningvisualaspects,wehaveshownthatatechniquecombiningGaborphase lightexperimentshaveshownthat3-dbiometricinformationischeaplyaccessible. Furtherstudiestoimproveitsprecisionisunderway.Biometricfeaturesconcerning thefacialfeaturesintermsofinterdistancesofeyes,mouthandnosenecessitatea 6.CONCLUSION verication. sequences,suchasproles[2],couldbeusedtoenhancethecapacityoftheimagebased basedttingisawaytoimprovetherobustness.othereasilyavailabledatafromvideo robustlocalizationofthesefeatures.ourtestsindicatethatmodel(suchasellipsoids) lettingaugurgoodresultsforthefusionwithfacefeatureswearecurrentlyrealizing. database. individuallyachievepromisingperformance. Alsoforeseenisthetestorestimationofperformanceachievableofalargemulti-modal Thecombinationofvoicerecognitionmethodsachievesanincreaseofperformances, Withrespecttovoice,wehaveshownthat3dierentrecognitionmethodscan [1]B.S.Atal.\Eectivenessoflinearpredictioncharacteristicsofthespeechwave [2]C.Beumier,M.P.Acheroy,\AutomaticFaceIdentication",InApplicationsof forautomaticspeakeridenticationandverication".jasa,vol.55,no.6,pp. DigitalImageProcessingXVIII,SPIE,vol.2564,pp ,July(1995) 1304{1312,(1974). 7.REFERENCES [3]J.Bigun,G.H.GranlundandJ.WiklundMultidimensionalorientationestimation [5]F.BimbotandL.Mathan.\Second-orderstatisticalmeasuresfortext-independant [4]J.BigunAstructurefeatureforimageprocessingapplicationsbasedonspiral withapplicationstotextureanalysisandopticalowieee-pamivol.13,no.8, speakeridentication".inesca[10],pp.51{54. (1990) functionscomputervision,graphicsandimageprocessing.no.51,pp pp (1991). [6]R.BrunelliandD.Falavigna.\Personidenticationusingmultiplecues".IEEE [7]Y.Dai,Y.Nakano,\ExtractionofFacialImagesfromComplexBackgroundUsing TransactionsonPatternAnalysisandMachineIntelligence,Vol.17,No.10,pp. [8]BelurV.Dasarathy.DecisionFusion.IEEEComputerSocietyPress,LosAlamitos,California, {966,October(1995). ColorInformationandSGLDMatrices,"Int.WorkshoponAutomaticFace-and GestureRecognition,pp ,ed.MartinBichsel,Zurich,Switzerland,June

14 [10]ESCA,editor.ESCAWorkshoponAutomaticSpeakerRecognitionIdentication [11]A.Eleftheriadis,A.Jacquin,\Automaticfacelocationandtrackingformodel- [9]K.Drouiche.ChapitreIV:Testdesphericite.PhDthesis,ENST,Jan.(1993). [12]G.Galicia,A.Zakhor,\DepthBasedRecoveryofHumanFacialFeaturesfrom Verication.ESCA,April(1994). [13]G.G.Gordon,\Facerecognitionbasedondepthmapsandsurfacecurvature",In cessing:imagecommunication,vol.7,no.3,pp ,july(1995). assistedcodingofvideoteleconferencingsequencesatlowbitrates,"signalpro- [14]M.Homayounpour.Vericationdulocuteur:Dependanteetindependantedutexte. VideoSequences,"IEEEConf.onImageProcessing,vol.2,pp ,Washington,USA,October(1995). [15]R.A.Jarvis,\Aperspectiveonrangendingtechniquesforcomputervision",In GeometricmethodsinComputerVision,SPIE,vol1570,SanDiego(1991). [16]D.Genoud,G.Gravier,F.Bimbot,M.Homayounpour,andG.Chollet. PhDthesis,UniversitePARIS-SUD,(1995). IEEETransactionsonPatternAnalysisandMachineIntelligence,vol.PAMI-5, [17]M.Lades,J.BuhmannJ.C.Vorbruggen,J.Lange,C.v.d.Malsburg,R.P.Wurtz, demethodes.acceptedforxxiemesjourneesd'etudessurlaparole, pp ,march(1983). Avignon10-14Juin1996. Ameliorationdesperformancesdereconnaissancedulocuteurparcombinaison [18]H.Niemann,\PatternAnalysisandUnderstanding",Springer-Verlag,(1990). [19]I.Pitas,A.N.Venetsanopoulos,\NonlinearDigitalFilters:PrinciplesandApplications",KluwerAcademicPublishers,(1990). andw.konen.\distortioninvariantobjectrecognitioninthedynamiclinkarchitecture.".ieeetransactionsoncomputers,vol.42,no.3,pp.300{311,march [20]DominiqueGenoudandGerardChollet.Polycodeavericationdatabase.Technicalreport,IDIAP,CH-1920Martigny,1995. (1993). [22]A.E.Rosenberg&C.H.Lee&S.Gokoen.Connectedwordtalkervericationusing [21]D.A.Reynolds.AGaussianmixturemodelingapproachtotext-independent speakeridentication.phdthesis,georgiainstituteoftechnology,(1992). wholewordhiddenmarkovmodel.inicassp-91,pages381{384,1991. [23]P.Thevenaz.Residudepredictionlineaireetreconnaissancedelocuteurs independantedutexte.phdthesis,universitedeneuch^atel,(1993).

15 [24]H.Wu,Q.Chen,M.Yachida,\AnApplicationofFuzzyTheory:FaceDetection,"Int.WorkshoponAutomaticFace-andGestureRecognition,pp , ed.martinbichsel,zurich,switzerland,june(1995). HarriDeutsch,Thun,FrankfurtamMain,(1995). [25]R.P.Wurtz.MultilayerDynamicLinkNetworksforEstablishingImagePoint CorrespondencesandVisualObjectRecognition,Vol.41ofReihePhysik.Verlag

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