文献翻译——二维人脸识别

第1页外文文献资料Wirelesssensornetworks1.FeatureLocalizationBeforediscussingthemethodsofcomparingtwofacialimageswenowtakeabrieflookatsomeatthepreliminaryprocessesoffacialfeaturealignment.Thisprocesstypicallyconsistsoftwostages:facedetectionandeyelocalisation.Dependingontheapplication,ifthepositionofthefacewithintheimageisknownbeforehand(foracooperativesubjectinadooraccesssystemforexample)thenthefacedetectionstagecanoftenbeskipped,astheregionofinterestisalreadyknown.Therefore,wediscusseyelocalisationhere,withabriefdiscussionoffacedetectionintheliteraturereview.Theeyelocalisationmethodisusedtoalignthe2Dfaceimagesofthevarioustestsetsusedthroughoutthissection.However,toensurethatallresultspresentedarerepresentativeofthefacerecognitionaccuracyandnotaproductoftheperformanceoftheeyelocalisationroutine,allimagealignmentsaremanuallycheckedandanyerrorscorrected,priortotestingandevaluation.Wedetectthepositionoftheeyeswithinanimageusingasimpletemplatebasedmethod.Atrainingsetofmanuallypre-alignedimagesoffacesistaken,andeachimagecroppedtoanareaaroundbotheyes.Theaverageimageiscalculatedandusedasatemplate.Figure1-1-Theaverageeyes.Usedasatemplateforeyedetection.Botheyesareincludedinasingletemplate,ratherthanindividuallysearchingforeacheyeinturn,asthecharacteristicsymmetryoftheeyeseithersideofthenose,providesausefulfeaturethathelpsdistinguishbetweentheeyesandotherfalsepositivesthatmaybepickedupinthe第2页background.Althoughthismethodishighlysusceptibletoscale(i.e.subjectdistancefromthecamera)andalsointroducestheassumptionthateyesintheimageappearnearhorizontal.Somepreliminaryexperimentationalsorevealsthatitisadvantageoustoincludetheareaofskinjustbeneaththeeyes.Thereasonbeingthatinsomecasestheeyebrowscancloselymatchthetemplate,particularlyifthereareshadowsintheeye-sockets,buttheareaofskinbelowtheeyeshelpstodistinguishtheeyesfromeyebrows(theareajustbelowtheeyebrowscontaineyes,whereastheareabelowtheeyescontainsonlyplainskin).Awindowispassedoverthetestimagesandtheabsolutedifferencetakentothatoftheaverageeyeimageshownabove.Theareaoftheimagewiththelowestdifferenceistakenastheregionofinterestcontainingtheeyes.Applyingthesameprocedureusingasmallertemplateoftheindividualleftandrighteyesthenrefineseacheyeposition.Thisbasictemplate-basedmethodofeyelocalisation,althoughprovidingfairlypreciselocalisations,oftenfailstolocatetheeyescompletely.However,weareabletoimproveperformancebyincludingaweightingscheme.Eyelocalisationisperformedonthesetoftrainingimages,whichisthenseparatedintotwosets:thoseinwhicheyedetectionwassuccessful;andthoseinwhicheyedetectionfailed.Takingthesetofsuccessfullocalisationswecomputetheaveragedistancefromtheeyetemplate(Figure1-2top).Notethattheimageisquitedark,indicatingthatthedetectedeyescorrelatecloselytotheeyetemplate,aswewouldexpect.However,brightpointsdooccurnearthewhitesoftheeye,suggestingthatthisareaisofteninconsistent,varyinggreatlyfromtheaverageeyetemplate.Figure1-2Distancetotheeyetemplateforsuccessfuldetections(top)indicatingvarianceduetonoiseandfaileddetections(bottom)showingcrediblevarianceduetomiss-detectedfeatures.Inthelowerimage(Figure1-2bottom),wehavetakenthesetoffailedlocalisations(imagesoftheforehead,nose,cheeks,backgroundetc.falsely第3页detectedbythelocalisationroutine)andonceagaincomputedtheaveragedistancefromtheeyetemplate.Thebrightpupilssurroundedbydarkerareasindicatethatafailedmatchisoftenduetothehighcorrelationofthenoseandcheekboneregionsoverwhelmingthepoorlycorrelatedpupils.Wantingtoemphasisethedifferenceofthepupilregionsforthesefailedmatchesandminimisethevarianceofthewhitesoftheeyesforsuccessfulmatches,wedividethelowerimagevaluesbytheupperimagetoproduceaweightsvectorasshowninFigure1-3.Whenappliedtothedifferenceimagebeforesummingatotalerror,thisweightingschemeprovidesamuchimproveddetectionrate.Figure1-3-Eyetemplateweightsusedtogivehigherprioritytothosepixelsthatbestrepresenttheeyes.2.TheDirectCorrelationApproachWebeginourinvestigationintofacerecognitionwithperhapsthesimplestapproach,knownasthedirectcorrelationmethod(alsoreferredtoastemplatematchingbyBrunelliandPoggio)involvingthedirectcomparisonofpixelintensityvaluestakenfromfacialimages.WeusethetermDirectCorrelationtoencompassalltechniquesinwhichfaceimagesarecompareddirectly,withoutanyformofimagespaceanalysis,weightingschemesorfeatureextraction,regardlessofthedistancemetricused.Therefore,wedonotinferthatPearsonscorrelationisappliedasthesimilarityfunction(althoughsuchanapproachwouldobviouslycomeunderourdefinitionofdirectcorrelation).WetypicallyusetheEuclideandistanceasourmetricintheseinvestigations(inverselyrelatedtoPearsonscorrelationandcanbeconsideredasascaleandtranslationsensitiveformofimagecorrelation),asthispersistswiththecontrastmadebetweenimagespaceandsubspaceapproachesinlatersections.Firstly,allfacialimagesmustbealignedsuchthattheeyecentresarelocatedattwospecifiedpixelcoordinatesandtheimagecroppedtoremoveanybackgroundinformation.Theseimagesarestoredasgreyscalebitmapsof65by82第4页pixelsandpriortorecognitionconvertedintoavectorof5330elements(eachelementcontainingthecorrespondingpixelintensityvalue).Eachcorrespondingvectorcanbethoughtofasdescribingapointwithina5330dimensionalimagespace.Thissimpleprinciplecaneasilybeextendedtomuchlargerimages:a256by256pixelimageoccupiesasinglepointin65,536-dimensionalimagespaceandagain,similarimagesoccupyclosepointswithinthatspace.Likewise,similarfacesarelocatedclosetogetherwithintheimagespace,whiledissimilarfacesarespacedfarapart.CalculatingtheEuclideandistanced,betweentwofacialimagevectors(oftenreferredtoasthequeryimageq,andgalleryimageg),wegetanindicationofsimilarity.Athresholdisthenappliedtomakethefinalverificationdecision.2.1.VerificationTestsTheprimaryconcerninanyfacerecognitionsystemisitsabilitytocorrectlyverifyaclaimedidentityordetermineapersonsmostlikelyidentityfromasetofpotentialmatchesinadatabase.Inordertoassessagivensystemsabilitytoperformthesetasks,avarietyofevaluationmethodologieshavearisen.Someoftheseanalysismethodssimulateaspecificmodeofoperation(i.e.securesiteaccessorsurveillance),whileothersprovideamoremathematicaldescriptionofdatadistributioninsomeclassificationspace.Inaddition,theresultsgeneratedfromeachanalysismethodmaybepresentedinavarietyofformats.Throughouttheexperimentationsinthisthesis,weprimarilyusetheverificationtestasourmethodofanalysisandcomparison,althoughwealsouseFishersLinearDiscriminanttoanalyseindividualsubspacecomponentsinsection7andtheidentificationtestforthefinalevaluationsdescribedinsection8.Theverificationtestmeasuresasystemsabilitytocorrectlyacceptorrejecttheproposedidentityofanindividual.Atafunctionallevel,thisreducestotwoimagesbeingpresentedforcomparison,forwhichthesystemmustreturneitheranacceptance(thetwoimagesareofthesameperson)orrejection(thetwoimagesareofdifferentpeople).Thetestisdesignedtosimulatetheapplicationareaofsecuresiteaccess.Inthisscenario,asubjectwillpresentsomeformofidentificationatapointofentry,perhaps第5页asaswipecard,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,ifthepurposeoftheexperimentationistoevaluateandimproveamethodoffacerecognition,whichmaybeappliedtoarangeofapplicationenvironments,thenthetestdatashouldpresenttherangeofdifficultiesthataretobeovercome.Thismaymeanincludingagreaterpercentageofdifficultimagesthanwouldbeexpectedintheperceivedoperatingconditionsandhencehighererrorratesintheresultsproduced.Belowweprovidethealgorithmforexecutingtheverificationtest.Thealgorithmisappliedtoasingletestsetoffaceimages,usingasinglefunctioncalltothefacerecognitionalgorithm:CompareFaces(FaceA,FaceB).Thiscallisusedtocomparetwofacialimages,returningadistancescoreindicatinghowdissimilarthetwofaceimagesare:thelowerthescorethemoresimilarthetwofaceimages.Ideally,imagesofthesamefaceshouldproducelowscores,whileimagesofdifferentfacesshouldproducehighscores.第6页Everyimageiscomparedwitheveryotherimage,noimageiscomparedwithitselfandnopairiscomparedmorethanonce(weassumethattherelationshipissymmetrical).Oncetwoimageshavebeencompared,producingasimilarityscore,theground-truthisusedtodetermineiftheimagesareofthesamepersonordifferentpeople.Inpracticalteststhisinformationisoftenencapsulatedaspartoftheimagefilename(bymeansofauniquepersonidentifier).Scoresarethenstoredinoneoftwolists:alistcontainingscoresproducedbycomparingimagesofdifferentpeopleandalistcontainingscoresproducedbycomparingimagesofthesameperson.Thefinalacceptance/rejectiondecisionismadebyapplicationofathreshold.Anyincorrectdecisionisrecordedaseitherafalseacceptanceorfalserejection.Thefalserejectionrate(FRR)iscalculatedasthepercentageofscoresfromthesamepeoplethatwereclassifiedasrejections.Thefalseacceptancerate(FAR)iscalculatedasthepercentageofscoresfromdifferentpeoplethatwereclassifiedasacceptances.ForIndexA=0tolength(TestSet)ForIndexB=IndexA+1tolength(TestSet)Score=CompareFaces(TestSetIndexA,TestSetIndexB)IfIndexAandIndexBarethesamepersonAppendScoretoAcceptScoresListElseAppendScoretoRejectScoresListForThreshold=MinimumScoretoMaximumScore:FalseAcceptCount,FalseRejectCount=0ForeachScoreinRejectScoresListIfScoreThresholdIncreaseFalseRejectCountFalseAcceptRate=FalseAcceptCount/Length(AcceptScoresList)FalseRejectRate=FalseRejectCount/length(RejectScoresList)Addplottoerrorcurveat(FalseRejectRate,FalseAcceptRate)Thesetwoerrorratesexpresstheinadequaciesofthesystemwhen第7页operatingataspecificthresholdvalue.Ideally,boththesefiguresshouldbezero,butinrealityreducingeithertheFARorFRR(byalteringthethresholdvalue)willinevitablyresultinincreasingtheother.Therefore,inordertodescribethefulloperatingrangeofaparticularsystem,wevarythethresholdvaluethroughtheentirerangeofscoresproduced.TheapplicationofeachthresholdvalueproducesanadditionalFAR,FRRpair,whichwhenplottedonagraphproducestheerrorratecurveshownbelow.第8页Figure2-1-ExampleErrorRateCurveproducedbytheverificationtestTheequalerrorrate(EER)canbeseenasthepointatwhichFARisequaltoFRR.ThisEERvalueisoftenusedasasinglefigurerepresentingthegeneralrecognitionperformanceofabiometricsystemandallowsforeasyvisualcomparisonofmultiplemethods.However,itisimportanttonotethattheEERdoesnotindicatetheleveloferrorthatwouldbeexpectedinarealworldapplication.Itisunlikelythatanyrealsystemwoulduseathresholdvaluesuchthatthepercentageoffalseacceptanceswereequaltothepercentageoffalserejections.Securesiteaccesssystemswouldtypicallysetthethresholdsuchthatfalseacceptancesweresignificantlylowerthanfalserejections:unwillingtotolerateintrudersatthecostofinconvenientaccessdenials.Surveillancesystemsontheotherhandwouldrequirelowfalserejectionratestosuccessfullyidentifypeopleinalesscontrolledenvironment.ThereforeweshouldbearinmindthatasystemwithalowerEERmightnotnecessarilybethebetterperformertowardstheextremesofitsoperatingcapability.Thereisastrongconnectionbetweentheabovegraphandthereceiver第9页operating-haracteristic(ROC)curves,alsousedinsuchexperiments.Bothgraphsaresimplytwovisualisationsofthesameresults,inthattheROCformatusestheTrueAcceptanceRate(TAR),whereTAR=1.0FRRinplaceoftheFRR,effectivelyflippingthegraphvertically.AnothervisualisationoftheverificationtestresultsistodisplayboththeFRRandFARasfunctionsofthethresholdvalue.ThispresentationformatprovidesareferencetodeterminethethresholdvaluenecessarytoachieveaspecificFRRandFAR.TheEERcanbeseenasthepointwherethetwocurvesintersect.Figure2-2-ExampleerrorratecurveasafunctionofthescorethresholdThefluctuationoftheseerrorcurvesduetonoiseandothererrorsisdependantonthenumberoffaceimagecomparisonsmadetogeneratethedata.Asmalldatasetthatonlyallowsforasmallnumberofcomparisonswillresultsinajaggedcurve,inwhichlargestepscorrespondtotheinfluenceofasingleimageonahighproportionofthecomparisonsmade.Atypicaldatasetof720images(asusedinsection4.2.2)provides258,840verificationoperations,henceadropof1%EERrepresentsanadditional2588correctdecisions,whereasthequalityofasingleimagecouldcausetheEERtofluctuatebyupto0.28.第10页2.2.ResultsAsasimpleexperimenttotestthedirectcorrelationmethod,weapplythetechniquedescribedabovetoatestsetof720imagesof60differentpeople,takenfromtheARFaceDatabase.Everyimageiscomparedwitheveryotherimageinthetestsettoproducealikenessscore,providing258,840verificationoperationsfromwhichtocalculatefalseacceptanceratesandfalserejectionrates.TheerrorcurveproducedisshowninFigure2-3.Figure2-3-Errorratecurveproducedbythedirectcorrelationmethodusingnoimagepreprocessing.WeseethatanEERof25.1%isproduced,meaningthatattheEERthresholdapproximatelyonequarterofallverificationoperationscarriedoutresultedinanincorrectclassification.Thereareanumberofwell-knownreasonsforthispoorlevelofaccuracy.Tinychangesinlighting,expressionorheadorientationcausethelocationinimagespacetochangedramatically.Imagesinfacespacearemovedfarapartduetotheseimagecaptureconditions,despitebeingofthesamepersonsface.Thedistancebetweenimagesofdifferentpeoplebecomessmallerthantheareaoffacespacecoveredbyimagesofthesamepersonandhencefalseacceptancesandfalserejectionsoccurfrequently.Otherdisadvantagesincludethelargeamountof第11页storagenecessaryforholdingmanyfaceimagesandtheintensiveprocessingrequiredforeachcomparison,makingthismethodunsuitableforapplicationsappliedtoalargedatabase.Insection4.3weexploretheeigenfacemethod,whichattemptstoaddresssomeoftheseissues.第12页中文翻译稿二维人脸识别1.功能定位在讨论比较两个人脸图像，我们现在就简要介绍的方法一些在人脸特征的初步调整过程。这一过程通常两个阶段组成：人脸检测和眼睛定位。根据不同的申请时，如果在面部图像的立场是众所周知事先（对于合作的主题，例如在门禁系统），那么人脸检测阶段通常可以跳过，作为地区的利益是已知的。因此，我们讨论眼睛定位在这里，有一个人脸检测的文献简短讨论。眼睛定位方法用于对齐的各种测试二维人脸图像集通篇使用这一节。但是，为了确保所有的结果都呈现代表面部识别准确率，而不是对产品的性能例行的眼睛定位，所有图像路线是手动检查，若有错误更正前的测试和评价。我们发现在一个使用图像的眼睛一个简单的基于模板的位置方法。训练集的前脸手动对齐图像是采取和各图片进行裁剪，以两只眼睛周围的地区。平均计算，用形象作为一个模板。图1-1-平均眼睛，用作模板的眼睛检测两个眼睛都包括在一个模板，而不是单独为每个搜索，因为眼睛的任一鼻子两边对称的特点，提供了一个有用的功能，可以帮助区分眼睛和其他可能误报被拾起的背景。虽然这种方法在介绍了假设眼近水平的形象出现后很容易受到规模（即主体距离相机）的影响，但一些初步试验还显示，还是有利于包括眼睛下方的皮肤区域得，因为在某些情况下，眉毛可以密切配合模板，特别是如果有在眼插座的阴影。此外眼睛以下的皮肤面积有助于区分从眉毛（眉毛下方的面积眼中包含的眼睛，而该地区眼睛下面的皮肤只含有纯）。窗口是通过对测试图像和绝对差采取的这一平均眼睛上面显示的图像。图像的最低差额面积作为含有眼中感兴趣的区域。运用同样的程序使用小模板个人左，右眼，然后提炼每只眼睛的位置。这个基本模板的眼睛定位方法，尽管提供相当精确的本地化，往往不能找到完全的眼睛。但是，我们能够改善计划包括加权性能。眼睛定位是在执行训练图像，然后被分成集两套：在哪些眼检测成功的，和那些在第13页其中眼检测失败的。以成功的本地化设置，我们在计算平均距离眼睛模板（图1-2顶部）时，请注意，该图像是非常黑暗的，这表明发现眼睛密切相关的眼睛模板，正如我们期望的那样。然而，亮点确实发生靠近眼睛的白人，这表明这方面经常是不一致的，不同于普通模板。图1-2-距离对眼睛模板成功检测（左），指出由于方差噪音和失败的检测（右）显示，由于错过可信的差异，检测功能。在较低的图像（图1-2左），我们已经采取了失败的本地化设置（在前额，鼻子图像，脸颊，背景等虚假的检测本地化例程），并再次从眼睛计算的平均距离模板。明亮的学生由暗区包围表明，一个失败的匹配往往由于鼻子和颧骨地区绝大多数的高相关性差相关的学生。想强调地区差异的学生为这些失败的比赛，尽量减少对眼睛的白人成功的变异比赛中，我们除以上的形象价值较低的图像产生重矢量，如图1-3所示。当应用到差分图像在总结前一总误差，这个比重计划提供了一个很大的提高检出率。图1-32.直接相关方法我们把最简单的方法人脸识别调查称为直接相关方法（也称为模板匹配的布鲁内利和波焦）所涉及的像素亮度值直接比较取自面部图像。我们使用的术语直接关系，以涵盖所有在图像技术所面临的直接比较，没有任何形式的形象空间分析，加权计划或特征提取，无论距离度量使用。因此，我们并不推断，皮尔逊的相关性，作为应用相似的功能（尽管这种做法显然会受到我们的直接相关的定义）。我们通常使用欧氏距离度量作为我们的在这些调查（负相关，Pearson相关，可以考虑作为一个规模和翻译的图像相关敏感的形式），因为这与坚持对比了空间和子空间与图像的方法在后面的章节。首先，所有的面部图像必须保持一致，这样的眼睛在两个中心位于指定的像素坐标和裁剪，以消除任何背景的图像信息。这些图像存储为65和82像素灰度位图前进入了5330元素（每个元素包含向量转换确认相应的像素强度值）。每一个对应的向量可以被认为是说明在5330点的三维图像空间。这个简单的原则很容易被推广到更大的照片：由256像素的图像256占用一个在65,536维图像空间，并再次指出，类似的图像占据接近点在该空间。同样，类似的面孔靠近一起在图像空间，而不同的面间距相距甚远。计算第14页欧几里得距离d，两个人脸图像向量（通常称为查询图像Q和画廊图像克），我们得到一个相似的迹象。然后用一个阈值，使最后核查的决定。2.1.验证测试任何一个人脸识别系统的主要关注的是它能够正确地验证声称的身份或确定一个人的最可能的身份从一个潜在的集合数据库中。为了评估一个给定的系统的能力来执行这些任务，采用不同的评价方法。其中的一些分析方法模拟一个具体的运作模式（即安全网站的访问或监视），而其他人提供更多的数据分布的数学描述中的一些分类空间。此外，每个分析结果产生的方法可能提交的各种格式。在本论文的整个实验，我们主要使用验证考验我们的方法分析和比较，虽然我们也使用费舍尔的线性判别分析在第7个个人组件和子空间鉴定试验中的第8条所述的最终评价。核查措施的测试系统的能力，正确地接受或拒绝一个人的身份提出。在一个功能级别，这样可以减少到两个图像正在为比较介绍，该系统必须对任何一个接受返回（两个图像是同一人）或拒绝（两个不同的图像人）。该测试旨在模拟安全网站访问的应用领域。在这种情况下，一个主题将在一入境点一些形式的身份证件，或许是刷卡，接近芯片或PIN号码。这个数字，然后用于检索数据库中的已知对象通常被称为目标（1存储的图像画廊或图像），并在入境点（捕获的现场图像比较查询图像）。访问是根据当时获得的接受/拒绝的决定。测试结果计算出多少倍的接受/拒绝决定是正确的。为了执行这项测试中，我们必须首先确定我们的测试人脸图像集。虽然这些图像的测试集的数量不会影响结果产生的误差（利率作为形象比较百分比指定），但重要的是要确保测试集是足够大，这样的统计异常变得不重要（例如，一个非常一致的匹配以及图像的情侣）。另外，影像的类型（照明高度变化，部分遮挡等）将显着改变的结果测试。因此，为了比较多个面部识别系统，他们必须适用于相同的测试集。但是，还应该指出，如果结果将系统性能的代表在现实世界中的情况，然后测试数据应根据被捕获正是在同样情况下的应用环境。另一方面，如果该实验的目的是评估和完善人脸识别方法，可应用到产品的应用范围环境，那么测试数据应目前的困难，要范围克服。这可能意味着包括一个难的图片比这个大的百分比可以预期的操作条件，因此被认为较高的错误率产生的结果。以下我们提供了执行验证测试算法。该算法适用于单个测试人脸图像集，使用一个函数调用在脸上识别算法：CompareFaces（FaceA，FaceB）。这一呼吁是用来比较两个面部图像，返回距离评分说明如何在两个不同的人脸图像为：得分越低越相似的两个人脸图像。理想情况下，图像的同样面对的是要生产低分数，而应产生不同的面孔图像高分。每一个形象，与所有其他形象相比，没有图像进行比较，并与自身没有一双比较不止一次（我们假设关系是对称的）。当两个图像进行比较，产生相似性评分，地面真相第15页用于确定是否对图像的同一人或不同的人。在实际这些信息往往是测试封装为图片文件名通过一个手段（部分独特的人标识符）。比分然后存储在两个列表之一：一份列出通过比较不同人的形象和产品清单，其中分数通过比较产生的同一人图像。最终的接受/拒绝决定是由一个门