外文翻译-基于半边脸的人脸检测

附录FACEDETECTIONBASEDONHALFFACETEMPLATEABSTRACTFACEDETECTIONINTHEIMAGEISANIMPORTANTRESEARCHBRANCHOFFACERECOGNITIONFORTHEPURPOSEOFDETECTINGTHEFACESINIMAGESEFFICIENTLY,AFACEDETECTIONMETHODBASEDONHALFFACETEMPLATEISPROPOSEDACCORDINGTOTHECHARACTEROFDENSITYOFTHEFEATUREORGANSSUCHASEYE,EAR,NOSE,MOUTH,PARTOFCHEEKINTHEFACEIMAGES,THEOBVERSEAVERAGEFULLFACETEMPLATEISCONSTRUCTEDANDTHEOBVERSEAVERAGEHALFFACETEMPLATEISCONSTRUCTEDDIRECTLYBASEDONDENSITYSYMMETRYOFFACETEMPLATETHEFACEDETECTIONEXPERIMENTSINTHEIMAGESWERECARRIEDONUSINGTHEMETHODOFTEMPLATEMATCHINGANDDETERMINETHEPOSITIONOFTHEFACEINTHEIMAGEACCORDINGTOCOMPARABILITYTHETHEORYANALYSISSHOWSTHATTHEAVERAGEFACETEMPLATECANREDUCETHECHANCINESSOFLOCALDENSITYOFTHETEMPLATEEFFECTIVELYANDTHEHALFFACETEMPLATECANREDUCETHESYMMETRYREDUNDANCYOFDENSITYINTHEFACETEMPLATEANDINCREASETHESPEEDOFFACEDETECTIONTHEEXPERIMENTALRESULTINDICATESTHATTHEHALFFACETEMPLATECANADAPTTOSIDEFACEIMAGESINALARGEANGLE,WHICHIMPROVESTHECORRECTNESSOFSIDEFACEDETECTIONSUBSTANTIALLYKEYWORDSFULLFACETEMPLATEHALFFACETEMPLATETEMPLATEMATCHINGCOMPARABILITY,SIDEFACEIINTRODUCTIONFACERECOGNITIONISTHEHOTTOPICINTHERESEARCHFIELDOFIMAGEPROCESSINGANDRECOGNITIONANDCOMPUTERVISIONINRECENTYEARSASONEOFTHEIMPORTANTSTEPSOFFACERECOGNITION,FACEDETECTIONHASBEENANEXTENSIVERESEARCHFIELDTHEMAINPURPOSEOFFACEDETECTIONISTODETERMINETHEINFORMATIONSUCHASWHETHERTHEREEXISTSFACEANDTHEPOSITION,THEROTATIONANDTHEPOSEOFTHEFACEINTHEIMAGEACCORDINGTOTHEDIFFERENTFEATURESOFTHEFACE,THEMETHODOFFACEDETECTIONVARIES14ANDACCORDINGTOTHEFIXEDLAYOUTOFTHECOLORORTHEDENSITYOFFACEORGAN,WECANDETERMINEWHETHERTHEREEXISTSAFACESOTHEMETHODBASEDONSKINCOLORMODELSANDTEMPLATEMATCHINGISTHEIMPORTANTRESEARCHDIRECTIONOFFACEDETECTION57THEFACEDETECTIONMETHODBASEDONTEMPLATEMATCHINGCHOOSESFULLFACEFEATUREASTHEMATCHEDTEMPLATE,WITHWHICHTHEBURDENOFCOMPUTINGOFFACESEARCHISRELATIVELYLARGEHOWEVER,MOSTHUMANFACESARESYMMETRYOBVIOUSLYSOWECANCHOOSEHALFOFTHEFULLFACETEMPLATETHATISCHOOSINGTHELEFTHALFFACEORTHERIGHTHALFFACEASTHETEMPLATEOFFACEMATCHINGWHICHCANREDUCETHEBURDENOFCOMPUTINGOFFACESEARCHIIFACETEMPLATECONSTRUCTINGMETHODTHEQUALITYOFTEMPLATEIMMEDIATEINFLUENCESTHEEFFECTOFMATCHINGDETECTIONTOREDUCETHECHANCINESSOFLOCALDENSITYOFTHETEMPLATE,THETEMPLATEBASEDONTHEINFORMATIONOFAVERAGEFACEISCONSTRUCTED,SUCHASAVERAGEEYETEMPLATEANDAVERAGEFACETEMPLATETHISMETHODISVERYEASYATTHEINSTANCEOFTHEAFFINETRANSFORMATIONOFTHETEMPLATE,THEFACEDETECTIONEFFICIENCYWILLBEVERYCERTIFIABLETHEPROCESSOFCONSTRUCTINGOBVERSEAVERAGEFACETEMPLATEASFOLLOWS8STEP1CHOOSETHEOBSERVEFACEIMAGESSTEP2DETERMINETHESIZEOFFACEAREAANDSELECTFACEAREASTEP3NORMALIZESELECTEDFACEAREASINTOTHESAMESIZESTEP4COMPUTETHEAVERAGEVALUEOFEVERYCORRESPONDINGPIXELOFFACEAREABEFORECONSTRUCTINGTEMPLATE,SOMEIMAGESINCLUDINGOBSERVEFACEARECHOSENATFIRST,THESIZEOFFACEAREAISDETERMINEDTHEN,THEFACEAREASELECTEDMANUALLYINTHESEFACEIMAGETHENUMBEROFTHESELECTEDFACEAREAISNTHEMATRIXVECTORSOFFACEAREASAREDISTRIBUTEDINDEPENDENTLY,SOARETHEPIXELVALUESINSAMEPOSITIONOFTHESEFACEAREASTHEPIXELVALUEINTHEKTHK1,2,NPOSITIONOFFACEAREAISFKI,JK1,2,NANDTHENORMALIZEDSCALEFACTOROFTHESEFACEIMAGEISWKK1,2,N,THUS,THEAVERAGEFACETEMPLATECANBEDESCRIBEDASFOLLOWSACCORDINGTOSTATISTICSTHEORY,IFSOMEPIXELVALUEFKI,JINTHEKTHFACEAREASUBJECTSTONORMALDISTRIBUTION,WHERE,ISTHEMEANOFTHEPIXELFKI,JANDISTHEVARIANCE,TI,JSUBJECTSTODISTRIBUTIONSOTHECHANCINESSOFLOCALDENSITYINFACETEMPLATEDECREASESINALARGESCALEIFFACEIMAGEISSAMPLEDINTHESITUATIONTHATTHEDISTANCEBETWEENTHEFACEANDTHECAMERAISFIXED,THESIZEOFFACEISUNCHANGEDCORRESPONDINGLYANDTHENORMALIZEDSCALEFACTORWKCANBE1THENTHEAVERAGEFACETEMPLATETI,JCANBEALTEREDASFOLLOWSIIIAVERAGEFULLFACETEMPLATECONSTRUCTIONINTHESITUATIONTHATTHEDISTANCEBETWEENTHEFACEANDTHECAMERAISFIXEDANDTHEANGLEOFAIRSCAPESIS15,120FACEIMAGESARESAMPLEDATTHEANGLEOFOBVERSE,LEFTSIDE30AND45FOREACHANGLE,THEREARE40IMAGES,ANDTHENUMBEROFTHEFACEIMAGEWITHANDWITHOUTCAPAREBOTH20THESAMPLEDIMAGESARESHOWNASFIGURE1OBVERSE30FROMTHELEFTSIDE45FROMTHELEFTSIDEFIG1FACEIMAGESOFDIFFERENTANGLESINTHEIMAGE,THEOBVERSEFACEINCLUDESTHEFEATUREORGANSLIKEEYE,EAR,NOSE,MOUTH,PARTOFCHEEKANDSOON,ASISSHOWNINFIGURE2ATHECHARACTEROFDISTRIBUTIONOFTHESEIMAGESCANBETHEBASISFORDETECTINGTHEEXISTENCEOFFACESOTHEEYES,EARS,NOSES,MOUTHSANDPARTOFCHEEKWERESELECTEDASTHEMAINAREAOFCONSTRUCTINGTHEFULLFACETEMPLATE,WHOSEMODELISSHOWNINFIGURE2BTHISMETHODCANRULEOUTTHEINFLUENCEOFABNORMITYAREAANDTHENONHUMANONTICFEATUREONES,LIKECAP,BEARDANDSOONFIG2CHARACTERISTICORGANSMODELOFFACE16FACEIMAGESWERESAMPLEDMANUALLYTHESIZEOFEACHIMAGEIS2226PIXELSASACOMPARISONEXPERIMENT,TEMPLATEMUSTNOTONLYMATCHTHEOBVERSEIMAGES,BUTALSOTHESIDEONESSOTHETEMPLATECOULDNOTBETOOWIDETHEOBVERSEFULLFACETEMPLATECONSTRUCTEDISSHOWNINFIGURE3THEOBVERSETEMPLATEISPRIMELYREPRESENTATION,SOTHEOBVERSEAVERAGEFULLFACETEMPLATEWASJUSTCONSTRUCTEDFIG3AVERAGETEMPLATECONSTRUCTINGOFOBVERSEFULLFACEIVAVERAGEHALFFACETEMPLATECONSTRUCTIONTHEOBVERSEAVERAGEFULLFACETEMPLATEMAYBEREGARDASCOMBINATIONOFTHEALMOSTSYMMETRICALLEFTFACETEMPLATEANDTHERIGHTONESOTHEOBVERSEFULLFACETEMPLATECANBEDIVIDEDINTOTHELEFTFACETEMPLATEANDTHERIGHTONEATTHEAXISCENTEROFSYMMETRYTHECONSTRUCTIONOFHALFFACETEMPLATEISSHOWNASFIGURE4FURTHERMORE,THEAVERAGEHALFFACETEMPLATECANBECONSTRUCTEDBASEDONTHEAVERAGEFULLFACETEMPLATE,WHICHCANREDUCETHESYMMETRYREDUNDANCYOFDENSITYINTHEFULLFACETEMPLATETHEMETHODISSHOWNASFIGURE5FIG4MODELOFHALFFACETEMPLATECONSTRUCTINGFIG5HALFFACEAVERAGETEMPLATECONSTRUCTINGTHEDENSITYOFTHELEFTFACEANDTHERIGHTONEARESYMMETRICALINTHEPERFECTFACETEMPLATE,INOTHERWORDS,THEYARECOMPARABILITYPAIRSINTHEPRACTICE,THEREISALITTLEDIFFERENCEBETWEENTHELEFTFACEANDTHERIGHTONEINTHEFACEIMAGES,ANDTHEDISTRIBUTIONOFDENSITYOFTHEMISNOTAFULLYSYMMETRICALSCENE,SOTHECOMPARABILITYISDECREASEDTAKINGTHELEFTFACEFOREXAMPLE,THELEFTFACECANBEDETECTEDATFIRST,WHENSEARCHINGTHEFACEIMAGEWITHTHEAVERAGEHALFFACETEMPLATEEXAMINEWHETHERTHEREEXISTSFACEIMAGE,ASISSHOWNINFIGURE6ATHESOLIDLINEFRAMEINTHEFIGUREISTHELEFTFACEDETECTEDDETECTINGTHELOCATIONOFTHERIGHTFACEWITHTHELEFTFACETEMPLATE,THEDETECTEDPOSSIBLELOCATIONSOFTHERIGHTFACEARESHOWNINFIGURE6B,WHICHISMARKEDWITHTHEBROKENLINEFIG6DETECTEDPOSSIBLELOCATIONOFHALFFACEVCRITERIONFUNCTIONINTHEEXPERIMENT,THEHALFFACESINTHEIMAGESWEREDETECTEDWITHTHETEMPLATEMATCHINGMETHODITSFUNDAMENTALPRINCIPLECANBEDESCRIBEDASFOLLOWSTHESELECTEDAVERAGEHALFFACETEMPLATEISRANSACKEDONTHEDETECTINGIMAGEANDTHESIMILARITYBETWEENTHEMISCALCULATEDTHEHALFFACEIMAGEWHICHISSIMILARTOTHETEMPLATEISRECKONEDAMONGTHEDETECTINGIMAGE,IFTHEVALUEOFTHESIMILARITYFUNCTIONINSOMEPOSITIONISGREATERTHANTHETHRESHOLDVALUEGIVENTHESIMILARITYISTHESTATISTICALVALUEOFLOCALAREASINTHEIMAGETHESIMILARITYVALUEOFSOMEDIFFERENTSUBIMAGEIMAGEMIGHTBEEQUALTOANOTHER,DESPITEALLTHATTHEYAREDIFFERENTSUBIMAGESINTHEEXPERIMENT,THESIMILARITYFUNCTIONVALUEINTHEHALFFACETEMPLATEMATCHEDPOSITIONSHOULDBESTOODOUTANDTHEVALUEINTHENONMATCHEDPOSITIONSHOULDBEBATEDTHEMETHODADOPTEDISDESCRIBEDASFOLLOWSSUPPOSETHELENGTHOFTHEHALFFACETEMPLATETISIANDTHEWIDTHISJ,ASISSHOWNINFIGURE4THELENGTHOFTHEFULLFACETEMPLATEIS2IANDTHEWIDTHISJOFTHEDETECTINGIMAGE,THELENGTHISLANDTHEWIDTHISWTHESUBIMAGECORRESPONDINGTOTHEPOSITIONM,NINTHEIMAGEWHERETHETEMPLATEISSETONISTHENTHESIMILARITYBETWEENTHETEMPLATEANDTHESUBIMAGESM,NCANBEEXPRESSEDWITHTHEFORMULA39FORTHEFUNCTIONABOVE,THERULEOFDETERMININGWHETHERTHEREEXISTSTHEHALFFACEISTHATGIVENATHRESHOLDVALUETH,IFSM,NTH,THEHALFFACETEMPLATETISSIMILARTOTHESUBIMAGE,ANDIFSM,N0,THEHALFFACETEMPLATETISIDENTICALWITHTHESUBIMAGECOMPLETELYEXPANDINGTHEFORMULA3,ITWILLBETHEFOLLOWINGWHERE,ISTHEENERGYOFSUBIMAGEWHICHISCOVEREDBYHALFFACETEMPLATETIIJJJIPNM12,LOCATEDINI,JOFTHEIMAGESDURINGTHERANSACKINGOFTHEIMAGES,ITSVALUEISCHANGINGSLOWLYISTHECORRELATIONBETWEENTHETEMPLATETANDSUBIMAGEITGETSTHEJITJIIIJJNM,1MAXIMUMVALUEWHENTEMPLATETMATCHTHESUBIMAGEEXACTLYISTHEENERGYOFHALFFACETEMPLATET,ITSVALUECANBEDETERMINEDWHENTHETEMPLATETISCONSTRUCTEDITHASNORELATIONSHIPWITHTHEPOSITIONOFTHESUBIMAGETHEREFORE,THERATIOOFTHECORRELATIONOFTHETEMPLATETANDSUBIMAGETOTHEENERGYOFSUBIMAGECANACTADTHESIMILARITY,THATIS,NORMALIZINGFORMULA5,THEFOLLOWINGONECOULDBEDEDUCEDWHERE,SM,NISSIMILARITY,ASTOSIMILARITYSM,N,THERULEFORDETERMININGWHETHERTHEHALFFACEEXISTSCANBEDESCRIBEDASIFTHRESHOLDISGIVENASTH,WHENSM,NTH,THECONCLUSIONTHATTHEHALFFACETEMPLATETISSIMILARTOTHESUBIMAGEISDRAWN,WHENSM,N1,THEHALFFACETEMPLATETMATCHTHESUBIMAGEEXACTLYASSUMINGTHATTHEOTREPRESENTSTIMECOSTOFFACEDETECTIONBASEDONHALFFACETEMPLATEANDTHEOFREPRESENTSTHATBASEDONFULLFACEONE,THECALCULATINGMETHODOFTHEOTANDOFCANBEDESCRIBEDASFORMULAS7AND8OTIJLIWJ7OF2IJL2IWJ8THERATIOOFOTTOOFCANBESHOWNINFORMULA9WHENTHEVALUEOFLISMUCHGREATERTHANTHATOFI,THERESULTOF9APPROXIMATESTO1/2,THATISTOSAY,THETIMECOSTOFDETECTINGFACESINIMAGESWITHTHEHALFFACETEMPLATEISABOUTHALFOFTHATWITHTHEFULLFACEONETHEREFORE,ABOUTHALFTIMEOFTHEPROCESSINGISSAVEDVIEXPERIMENTSRESULTSASTOAVERAGEHALFFACETEMPLATESHOWNINFIGURE5,THESIMILARITYOF“LEFTFACEANDLEFTFACE”,“RIGHTFACEANDRIGHTFACE”AND“LEFTFACEANDRIGHTFACE”WERECALCULATEDWITHTHEFORMULA6THERESULTSARE1000010000AND09535RESPECTIVELYTHECONCLUSIONCOULDBEDRAWNTHATTHESIMILARITYVALUEOFSAMEHALFFACESISHIGH,ANDTHEDIFFERENTHALFFACESARESIMILARPAIRSSOTHEDENSITYREDUNDANCYOFLEFTFACETOTHATOFRIGHTFACEISVERYGREATFACESDETECTIONWERECARRIEDOUTANEXPERIMENTONTHEOBVERSEFACEIMAGE,THEANGLEOF30AND45ONESTHEWITHAVERAGELEFTFACETEMPLATETHERESULTSOFTHECORRECTDETECTIONARESHOWNINFIGURE7BESIDES,ASACOMPARISON,THEEXPERIMENTSWITHAVERAGEFULLFACETEMPLATEWERECARRIEDON,ANDTHECORRECTDETECTIONRESULTSARESHOWNINFIGURE8FIG7FACEDETECTIONRESULTSBASEDONTHEAVERAGELEFTFACETEMPLATEFIG8FACEDETECTIONRESULTSBASEDONTHEAVERAGEFULLFACETEMPLATETHECORRECTRATIOOFFACEDETECTIONISANIMPORTANTESTIMATIONRULESINFACEDETECTION,WHICHCANBECALCULATEDASFOLLOWS40FACEIMAGESFROMTHEANGLEOFOBVERSE,30FROMTHELEFTSIDEAND45FROMTHELEFTSIDEWEREDETECTEDTHENUMBERANDDETECTIONRATIOOFFACESDETECTEDCORRECTLYARESHOWNINTABLE1THERESULTSSHOWNINTABLE1CANBESUMMEDASFOLLOWING1THECORRECTRATIOOFFACEDETECTIONINOBVERSEIMAGEISVERYHIGH,BECAUSETHEAVERAGEFULLFACETEMPLATEWASCONSTRUCTEDBASEDONASERIESOFOBSERVEFACEIMAGES,ANDTHEAVERAGEHALFFACEONEDIDONBASISOFAVERAGEFULLFACETEMPLATE,ALLOFTHEMREPRESENTTHEFEATUREOFFACES2DETECTINGFACESINIMAGESFROMTHESIDESSUCHASLEFTSIDEATANANGLEOF30AND45WITHTHEAVERAGEFULLFACETEMPLATE,THECORRECTDETECTIONRATIODROPSRAPIDLY,WHICHISMAINLYBECAUSELOTSOFTHEINFORMATIONOFTHERIGHTFACESARELOSTWHENTHEFACESARETURNINGLEFT,ANDITISHARDTOFINDTHEINFORMATIONTHATCANMATCHWITHRIGHTSIDEWELLINFULLFACETEMPLATE3THECORRECTRATIOISHIGHRELATIVELYWHILETHESIDEFACESINIMAGESWEREDETECTEDWITHTHEAVERAGEHALFFACETEMPLATE,WHICHISMAINLYBECAUSEINFORMATIONLOSSOFLEFTSIDEISFEWERWHENFACESARETURNINGTOTHELEFTSIDE,SOTHATTHEYCANBEMATCHEDWELLWITHTEMPLATESHOWEVER,IFTHEANGLEOFTHESIDEISTOOLARGE,CORRECTRATIOOFDETECTIONWILLDECREASEGREATLY,WHICHISMAINLYBECAUSEINTHEPROCESSOFFACEIMAGESSAMPLING,THEPROCESSINWHICHOBJECTSOFFACESINTHREEDIMENSIONALSPACEMAPPINGTOPLANARIMAGESISANABNORMALONE,ANDITNOTONLYLOSESINFORMATIONOFPOSITIONDEPTHINTHREEDIMENSIONALSPACEBUTCHANGESTHEPOSITIONOFFACEORGANSONTHEIMAGEGREATLYASWELLSOTHESIMILARITYBETWEENTHEMISREDUCEDVIICONCLUSIONTHEAVERAGEFACETEMPLATESCANBECONSTRUCTEDBASEDONTHEFEATURESOFFACEIMAGESANDTHEFACESPOSITIONSWEREDETECTEDACCORDINGTOTHESIMILARITYBETWEENTHETEMPLATESANDTHEFACESOFDIFFERENTANGLESINIMAGESTHERESULTSOFTHEORETICALANALYSISANDEXPERIMENTALARESHOWNASFOLLOWS1EYES,EARS,NOSES,MOUTHSANDPARTOFCHEEKAREESSENTIALPARTSINCONSTRUCTINGFACETEMPLATES,WHOSEDENSITYDISTRIBUTIONFEATUREINFACEIMAGESCANACTASBASISOFFACEDETECTING2AVERAGEFACETEMPLATESCANREDUCETHECHANCINESSOFTHEDENSITYINFORMATIONOFLOCALFEATUREORGANSGREATLY3ONTHEBASISOFTHEAVERAGEFULLFACETEMPLATES,THEAVERAGELEFTFACETEMPLATESCANBECONSTRUCTEDACCORDINGTOTHESYMMETRYOFTHEPOSITIONOFORGANSONFACESANDRIGHTONESDODIRECTLYTHEREDUNDANCYOFTHEDENSITYINFORMATIONOFTHEFACETEMPLATEISREDUCEDGREATLY4ACCORDINGTOTHEANALYSISOFTIMECOMPLEXITYINFACEDETECTIONBASEDONMATCHINGTEMPLATES,THEVALUEOFHALFFACETEMPLATELIESINTHEFACTTHATCOMPARINGTOFULLFACETEMPLATES,HALFONESCANREDUCEABOUTHALFOFTHETIMECOMPLEXITYINFACEDETECTIONSOTHATTHEDETECTIONSPEEDCANBEINCREASED5AVERAGEHALFFACETEMPLATESCANADAPTTOFACEATGREATERANGLEANDINCREASETHECORRECTRATIOOFFACESDETECTIONREMARKABLYACKNOWLEDGMENTTHEAUTHORWISHESTOTHANKTHEIEEEFORPROVIDINGTHISTEMPLATEANDALLCOLLEAGUESWHOPREVIOUSLYPROVIDEDTECHNICALSUPPORTTHISSTUDYISSUPPORTEDBYTHEPROJECTOFSCIENCEANDTECHNOLOGYOFCHINAUNIVERSITYOFMININGANDTECHNOLOGYREFERENCES1HERIK“FACEDETECTIONASURVEY”COMPUTVISIMAGEUND,VOL83,PP236274,20012LHLIANG,HZAI,GYXU,ETAL“ASURVEYOFHUMANFACEDETECTION”CHINESEJCOMPUT,VOL25,NO5,PP449458,20023NSUN,CRZOU,LZHAO“FACEDETECTIONASURVEY”JOFCIRCUITSANDSYSTEMSVOL11,NO6,PP101113,20064MHYANG,DJKRIEGMAN,NAHUJA“DETECTINGFACESINIMAGESASURVEY”IEEETRANSONPATTERNANALMACHINTELL,VOL24,NO1,PP3458,20025ZHONGJIN,ZHENLOU,JINGYUYANG,ETAL“FACEDETECTIONUSINGTEMPLATEMATCHINGANDSKINCOLORINFORMATION”NEUROCOMPUTING,VOL70,PP794800,20076JMEYNET,VPOPOVICI,JPTHIRAN“FACEDETECTIONWITHBOOSTEDGAUSSIANFEATURES”PATTERNRECOGN,VOL40,PP22832291,20077LLHUANG,ASHIMIZU“AMULTIEXPERTAPPROACHFORROBUSTFACEDETECTION”PATTERNRECOGN,VOL39,PP16951703,20068LHLIANG,HZAI,KZHE,ETAL“SINGLEROTATEDFACELOCATIONBASEDONAFFINETEMPLATEMATCHING”CHINESEJCOMPUT,VOL23,NO6,PP640645,20009TSERGIOS,KKONSTANTINOSPATTERNRECOGNITIONTHIRDEDITIONELSEVIERINCPP3974252006基于半边脸的人脸检测WEICHENTONGFENGSUNXIAODONGYANGLIWANGELECTRONICMEASUREMENTINSTRUMENTS,2009ICEMI099THINTERNATIONALCONFERENCEONDIGITALOBJECTIDENTIFIER101109/ICEMI20095274642PUBLICATIONYEAR2009,PAGES454458摘要图像中的人脸检测是人脸识别研究中一项非常重要的研究分支。为了更有效地检测图像中的人脸，此次研究设计提出了基于半边脸的人脸检测方法。根据图像中人半边脸的容貌或者器官的密度特征，比如眼睛，耳朵，嘴巴，部分脸颊，正面的平均全脸模板就可以被构建出来。被模拟出来的半张脸是基于人脸的对称性的特点而构建的。图像中人脸检测的实验运用了模板匹配法和相似性从而确定人脸在图像中的位置。此原理分析显示了平均全脸模型法能够有效地减少模板的局部密度的不确定性。基于半边脸的人脸检测能降低人脸模型密度的过度对称性，从而提高人脸检测的速度。实验结果表明此方法还适用于在大角度拍下的侧脸图像，这大大增加了侧脸检测的准确性。关键词人脸模板；半边人脸模板；模板匹配法；相似性；侧脸1介绍近几年，在图像处理和识别以及计算机视觉的研究领域中，人脸识别是一个很热门的话题。作为人脸识别中一个重要的环节，人脸检测也拥有一个延伸的研究领域。人脸检测的主要目的是为了确定图像中的信息，比如，图像总是否存在人脸，它的位置，旋转角度以及人脸的姿势。根据人脸的不同特征，人脸检测的方法也有所变化14。而且，根据人脸器官的密度或颜色的固定布局，我们可以判定是否存在人脸。因此，这种基于肤色模型和模板匹配的方法对于人脸检测具有重要的研究意义57。这种基于模板匹配的人脸检测法是选择正面脸部的特征作为匹配的模板，导致人脸搜索的计算量相对较大。然而，绝大多数的人脸都是对称的。所以我们可以选择半边正面人脸模板，也就是说，选择左半边脸或者有半边脸作为人脸匹配的模板，这样，大大减少了人脸搜索的计算。2人脸模板构建的方法人脸模板的质量直接影响匹配识别的效果。为了减少模板局部密度的不确定性，构建人脸模板是基于大众脸的信息，例如，平均的眼睛模板，平均的脸型模板。这种方法很简单。在模板的仿射变换的实例中，人脸检测的有效性可以被确保。构建人脸模板的过程如下8步骤一选择正面人脸图像；步骤二决定人脸区域的大小和选择人脸区域；步骤三将选出来的人脸区域格式化成同一种尺寸大小；步骤四计算人脸区域相对应像素的平均值。在构建模板之前，挑选些有正面人脸的图片。首先，决定人脸区域的尺寸大小。然后，在图像中手动挑选人脸区域。我们设选定的人脸区域的数量为N。因为人脸区域的矩阵向量都是被独立分布的，所以在那些人脸图像相同位置的像素值也是独立分布的。我们设在人脸区域第KK1,2,N位置的像素值是FKI,JK1,2,N，那些人脸图像的是标准比例系数WKK1,2,N，由此得出正面人脸模板的表达式（21）根据统计学，如果在人脸区域第K个位置，有些像素值FKI,J趋于正态分布,其中U是像素FKI,J的平均值，是方差，TI,J是正态分布。所以模板局部密度的不确定性大大降低了。如果抽样的人脸图像都是在同一间距下拍摄的，相对应的人脸尺寸是一致的，标准的比例像素WK就等于1那么，大众人脸模板TI,J也就变成了（22）3正面的平均全脸模板的构建在人脸与相机间距相同，鸟瞰图的拍摄角度是15的情况下，120张人脸图像被取样，包含正面的，左侧倾斜30和左侧倾斜45。每种角度的图像都是40张。其中20张中的人戴帽子，2张没有戴帽子。被抽样的图像如图1所示。正面左侧倾斜30左侧倾斜45图31各个角度的人脸图像在图像中，正面人脸包括特征器管像眼睛，耳朵，鼻子，部分脸颊等等，如图2（A）所示。这些图像的分布特征可以作为检测人脸存在的根据。所以人的眼睛，耳朵，鼻子，嘴巴和部分脸颊都被选作可以构建整张正面人脸模板的主要区域，如图2（B）所示。这种方法可以排除异常区域和非人类特有物的影响，比如帽子，胡须等。图32人脸特有器官的模型手动取样16张人脸图像。每张图像都是2226像素。做为一个比较性的实验，模板不仅要匹配正面图像，还要匹配侧面图像。所以模板不能太宽。构建整张正面人脸模板如图32所示。通过16张正面人脸模板，正面的平均全脸模板就可以被构建出来。图33正面全脸的平均模板的构建4平均的半脸模板的构建正面的平均全脸模板可以被看做大径相同的左脸模板和有脸模板的结合体。所以正面的全脸模板可以被中心对称轴分成左脸模板和右脸模板。所以，半边脸模板的构建如图41所示。此外，平均半脸模板可以根据平均全脸模板的原理来构建。这样可以减低在全脸模板中密集度的对称冗余的问题。方法如图42所示。图41构建半脸模板的模型图42半脸平均模板的构建在一张完美的人脸模板中，左脸和右脸的密集度是对称的，也就是说，两半边脸是相似的一对。事实上，在一张人脸图像中左脸和右脸存在一些差异，两半边脸的器官密集度的分布也不是完全对称的，所以相似性就降低了。就拿左半边脸为例，当利用平均的半脸模板搜索人脸图像时，左半边脸会先被识别出来，如图43A所示。图中实线框内是被检测出的左脸，接着根据左脸模板来检测右半边脸的位置。被检测到的可能是右半边脸的位置如图43B所示，这些位置由虚线框标记出来。图43被检测出来的可能是半边脸的位置5判别函数在实验中，图像中半边脸被检测出来是运用了模板匹配的方法。此方法的基本原理解释如下。被选择的平均半脸模板在被检测的图像上到处搜索。接着，计算模板与被检测到的半边脸的相似度。如果在某些位置，相似度方程的值大于阈值，那么我们就认为这班别脸的图像相似于平均半脸模板。相似度是指图像上局部区域的统计值。一些不同的子块影像的图像相似度值也许与其他的一样，尽管如此，它们还是属于不同的字块图像。在实验中，与半脸模板匹配的子块图像所在位置的相似度值应该被保留，而那些未匹配的值应该被剔除。此方法具体可以描述如下假设半脸模板T的长度是I，宽是J，如图四所示。那么全脸模板的长度就是2I，宽是J。假设被检测的图像的长度是L，宽是W，当模板为放在M,N时，子块图像在图像中相对应的位置为。于是模板与子块图像SM,N的相似度的公式39可以表达为（51）上述公式中，判定图像中是否存在半边脸的规则是，给定一个阈值TH，如果SM,NTH,那么此半边脸相似于子块图像；如果SM,N0，那么此半边脸完全与子块图像一致。把公式（3）展开后，得到（52）其中，是子块的能，被位于图像I,J位置的半脸模板所覆盖。IIJJJIPNM12,在到处搜索图像的时候，它的值变化很慢。表示模板T与子块图像JITJIIIJJNMP,1的相关系数。当模板T与子块图像完全匹配时，相关系数达到最大值。是半脸模板T的能，当半脸模板被构建完后，它的值就被确定了。它与子IIJJ12,块的位置没有任何关系。所以，模板T与子块图像的关联系数和子块的能值的比率就是相似度值，如下（53）简化式子（53），得到式子（54）（54）其中，SM,N是相似度，。判定半边脸存在的规则如下给定阈值TH，如果SM,NTH,那么总结为此半脸模板T相似于子块图像；如果SM,N1，那么半脸入班T完全与子块图像匹配。假设OT代表基于半边脸模板检测人脸的时间花费，OF代表基于全脸模板检测人脸的时间花费。计算方法如下OTIJLIWJ55OF2IJL2IWJ46OTT与OF的比值如下57ILJILJIFOT422当L的值大大超过I值时，方式（9）的值接近1/2，也就是说，基于半边脸模板检测人脸的时间花费是全脸模板方法的1/2。所以，半边脸模板检测方法可以省一半的时间。6实验结果正如图五显示的平均半脸模板，左脸与左脸，右脸与右脸，左脸与右脸的相似度都是通过式子（6）计算得出。计算结果分别是10000，10000和09535。实验结果是相同半边脸的相似度值很高，不同半边