人脸识别文献翻译中英文.docx
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人脸识别文献翻译中英文
附录(原文及译文)
翻译原文来自
ThomasDavidHeseltineBSc.Hons.TheUniversityofYork
DepartmentofComputerScience
FortheQualificationofPhD.--September2005-
《FaceRecognition:
Two-DimensionalandThree-DimensionalTechniques》
4Two-dimensionalFaceRecognition
4.1FeatureLocalization
Beforediscussingthemethodsofcomparingtwofacialimageswenowtakeabrieflookatsomeatthepreliminaryprocessesoffacialfeaturealignment.Thisprocesstypicallyconsistsoftwostages:
facedetectionandeyelocalisation.Dependingontheapplication,ifthepositionofthefacewithintheimageisknownbeforehand(foracooperativesubjectinadooraccesssystemforexample)thenthefacedetectionstagecanoftenbeskipped,astheregionofinterestisalreadyknown.Therefore,wediscusseyelocalisationhere,withabriefdiscussionoffacedetectionintheliteraturereview(section3.1.1).
Theeyelocalisationmethodisusedtoalignthe2Dfaceimagesofthevarioustestsetsusedthroughoutthissection.However,toensurethatallresultspresentedare
representativeofthefacerecognitionaccuracyandnotaproductoftheperformanceoftheeyelocalisationroutine,allimagealignmentsaremanuallycheckedandanyerrorscorrected,priortotestingandevaluation.
Wedetectthepositionoftheeyeswithinanimageusingasimpletemplatebased
method.Atrainingsetofmanuallypre-alignedimagesoffacesistaken,andeach
imagecroppedtoanareaaroundbotheyes.Theaverageimageiscalculatedandused
asatemplate.
Figure4-1-Theaverageeyes.Usedasatemplateforeyedetection.
Botheyesareincludedinasingletemplate,ratherthanindividuallysearchingforeacheyeinturn,asthecharacteristicsymmetryoftheeyeseithersideofthenose,providesausefulfeaturethathelpsdistinguishbetweentheeyesandotherfalsepositivesthatmaybepickedupinthebackground.Althoughthismethodishighlysusceptibletoscale(i.e.subjectdistancefromthecamera)andalsointroducestheassumptionthateyesintheimageappearnearhorizontal.Somepreliminaryexperimentationalsorevealsthatitisadvantageoustoincludetheareaofskinjustbeneaththeeyes.Thereasonbeingthatinsomecasestheeyebrowscancloselymatchthetemplate,particularlyifthereareshadowsintheeye-sockets,buttheareaofskinbelowtheeyeshelpstodistinguishtheeyesfromeyebrows(theareajustbelowtheeyebrowscontaineyes,whereastheareabelowtheeyescontainsonlyplainskin).
Awindowispassedoverthetestimagesandtheabsolutedifferencetakentothatoftheaverageeyeimageshownabove.Theareaoftheimagewiththelowestdifferenceistakenastheregionofinterestcontainingtheeyes.Applyingthesameprocedureusingasmallertemplateoftheindividualleftandrighteyesthenrefineseacheyeposition.
Thisbasictemplate-basedmethodofeyelocalisation,althoughprovidingfairlypreciselocalisations,oftenfailstolocatetheeyescompletely.However,weareableto
improveperformancebyincludingaweightingscheme.
Eyelocalisationisperformedonthesetoftrainingimages,whichisthenseparatedintotwosets:
thoseinwhicheyedetectionwassuccessful;andthoseinwhicheyedetectionfailed.Takingthesetofsuccessfullocalisationswecomputetheaveragedistancefromtheeyetemplate(Figure4-2top).Notethattheimageisquitedark,indicatingthatthedetectedeyescorrelatecloselytotheeyetemplate,aswewouldexpect.However,brightpointsdooccurnearthewhitesoftheeye,suggestingthatthisareaisofteninconsistent,varyinggreatlyfromtheaverageeyetemplate.
Figure4-2–Distancetotheeyetemplateforsuccessfuldetections(top)indicatingvariancedueto
noiseandfaileddetections(bottom)showingcrediblevarianceduetomiss-detectedfeatures.
Inthelowerimage(Figure4-2bottom),wehavetakenthesetoffailedlocalisations(imagesoftheforehead,nose,cheeks,backgroundetc.falselydetectedbythelocalisationroutine)andonceagaincomputedtheaveragedistancefromtheeyetemplate.Thebrightpupilssurroundedbydarkerareasindicatethatafailedmatchisoftenduetothehighcorrelationofthenoseandcheekboneregionsoverwhelmingthepoorlycorrelatedpupils.Wantingtoemphasisethedifferenceofthepupilregionsforthesefailedmatchesandminimisethevarianceofthewhitesoftheeyesforsuccessfulmatches,wedividethelowerimagevaluesbytheupperimagetoproduceaweightsvectorasshowninFigure4-3.Whenappliedtothedifferenceimagebeforesummingatotalerror,thisweightingschemeprovidesamuchimproveddetectionrate.
Figure4-3-Eyetemplateweightsusedtogivehigherprioritytothosepixelsthatbestrepresenttheeyes.
4.2TheDirectCorrelationApproach
Webeginourinvestigationintofacerecognitionwithperhapsthesimplestapproach,knownasthedirectcorrelationmethod(alsoreferredtoastemplatematchingbyBrunelliandPoggio[29])involvingthedirectcomparisonofpixelintensityvaluestakenfromfacialimages.Weusetheterm‘DirectCorrelation’toencompassalltechniquesinwhichfaceimagesarecompareddirectly,withoutanyformofimagespaceanalysis,weightingschemesorfeatureextraction,regardlessofthedistancemetricused.Therefore,wedonotinferthatPearson’scorrelationisappliedasthesimilarityfunction(althoughsuchanapproachwouldobviouslycomeunderourdefinitionofdirectcorrelation).WetypicallyusetheEuclideandistanceasourmetricintheseinvestigations(inverselyrelatedtoPearson’scorrelationandcanbeconsideredasascaleandtranslationsensitiveformofimagecorrelation),asthispersistswiththecontrastmadebetweenimagespaceandsubspaceapproachesinlatersections.
Firstly,allfacialimagesmustbealignedsuchthattheeyecentresarelocatedattwospecifiedpixelcoordinatesandtheimagecroppedtoremoveanybackground
information.Theseimagesarestoredasgreyscalebitmapsof65by82pixelsandpriortorecognitionconvertedintoavectorof5330elements(eachelementcontainingthecorrespondingpixelintensityvalue).Eachcorrespondingvectorcanbethoughtofasdescribingapointwithina5330dimensionalimagespace.Thissimpleprinciplecaneasilybeextendedtomuchlargerimages:
a256by256pixelimageoccupiesasinglepointin65,536-dimensionalimagespaceandagain,similarimagesoccupyclosepointswithinthatspace.Likewise,similarfacesarelocatedclosetogetherwithintheimagespace,whiledissimilarfacesarespacedfarapart.CalculatingtheEuclideandistanced,betweentwofacialimagevectors(oftenreferredtoasthequeryimageq,andgalleryimageg),wegetanindicationofsimilarity.Athresholdisthenappliedtomakethefinalverificationdecision.
dqg(dthreshold⇒accept)(dthreshold⇒reject).Equ.4-1
4.2.1VerificationTests
Theprimaryconcerninanyfacerecognitionsystemisitsabilitytocorrectlyverifyaclaimedidentityordetermineaperson'smostlikelyidentityfromasetofpotentialmatchesinadatabase.Inordertoassessagivensystem’sabilitytoperformthesetasks,avarietyofevaluationmethodologieshavearisen.Someoftheseanalysismethodssimulateaspecificmodeofoperation(i.e.securesiteaccessorsurveillance),whileothersprovideamoremathematicaldescriptionofdatadistributioninsome
classificationspace.Inaddition,theresultsgeneratedfromeachanalysismethodmay
bepresentedinavarietyofformats.Throughouttheexperimentationsinthisthesis,weprimarilyusetheverificationtestasourmethodofanalysisandcomparison,althoughwealsouseFisher’sLinearDiscriminanttoanalyseindividualsubspacecomponentsinsection7andtheidentificationtestforthefinalevaluationsdescribedinsection8.Theverificationtestmeasuresasystem’sabilitytocorrectlyacceptorrejecttheproposedidentityofanindividual.Atafunctionallevel,thisreducestotwoimagesbeingpresentedforcomparison,forwhichthesystemmustreturneitheranacceptance(thetwoimagesareofthesameperson)orrejection(thetwoimagesareofdifferentpeople).Thetestisdesignedtosimulatetheapplicationareaofsecuresiteaccess.Inthisscenario,asubjectwillpresentsomeformofidentificationatapointofentry,perhapsasaswipecard,proximitychiporPINnumber.Thisnumberisthenusedtoretrieveastoredimagefromadatabaseofknownsubjects(oftenreferredtoasthetargetorgalleryimage)andcomparedwithaliveimagecapturedatthepointofentry(thequeryimage).Accessisthengranteddependingontheacceptance/rejectiondecision.
Theresultsofthetestarecalculatedaccordingtohowmanytimestheaccept/rejectdecisionismadecorrectly.Inordertoexecutethistestwemustfirstdefineourtestsetoffaceimages.Althoughthenumberofimagesinthetestsetdoesnotaffecttheresultsproduced(astheerrorratesarespecifiedaspercentagesofimagecomparisons),itisimportanttoensurethatthetestsetissufficientlylargesuchthatstatisticalanomaliesbecomeinsignificant(forexample,acoupleofbadlyalignedimagesmatchingwell).Also,thetypeofimages(highvariationinlighting,partialocclusionsetc.)willsignificantlyaltertheresultsofthetest.Therefore,inordertocomparemultiplefacerecognitionsystems,theymustbeappliedtothesametestset.
However,itshouldalsobenotedthatiftheresultsaretoberepresentativeofsystemperformanceinarealworldsituation,thenthetestdatashouldbecapturedunderpreciselythesamecircumstancesasintheapplicationenvironment.Ontheotherhand,ifthepurposeoftheexperimentationistoevaluateandimproveamethodof