[硕士论文精品]人脸检测中的训练集优化

G6700G16213I摘要人脸检测（FACEDETECTION）是指任意给定一幅图像，判定该图像中是否存在人脸；如果存在，则返回其位置和大小G452该研究具有重要的科学意义和巨大的应用价值，一些相关的应用主要包括人机交互、人脸识别和人脸数据库的管理，特别是与安全领域相关的视频监控等。经过三十多年的发展，一系列基于统计学习的人脸检测方法取得了长足进步并得到了广泛应用。但是所有这些基于统计学习的方法的性能都在很大程度上依赖于G16769G13463G19610G2524的G1260G2167。G3252G8504，研究G13785G1216G5460G5460G993得G993G14469G17165大G18339的G12946G2159G7481G6922G19610具有足G3827G1207G15932性的人脸和G19762人脸G7691G7424G19610G2524。但G6117G1216G2376G8892意到，在G6922G19610到一G1022特定的G7691G7424G19610G2524G2530，研究人G2604的主要G12946G2159G4613G17728G12239到了特G5461G6564取和分类G3132G16786计方法上，G2376G17751G4581关G8892所G6922G19610的G7691G7424G19610G2524是否G2524理G1209G2462如G1321G4557其进G15904G14270G2172G1260G2282等重要G19394G20076。基于G8504，G7424G7003G999G19388G19036G4557G7691G7424G19610G14270G2172G1260G2282G19394G20076G5332展研究，重G9869G6518G16764了G17902过重G18331G7691G7481G1260G2282人脸检测G16769G13463G19610G16280G8181和G7691G7424分G5079的方法。主要G17141G10498G5647G13479如G9911提出了基于遗传算法的人脸样本扩张方法G20330G1820G6564G1998了一G12193基于G17963G1268G12651法（GAGENETICALGORITHM）的人脸G7691G7424G6205G5364方法，用G7481G4557G6922G19610的人脸G7691G7424G19610G2524进G15904数G18339上的G1260G2282。具G1319G3332G16840，G20330G1820G17902过G6175G5049G6922G19610一定数G18339的人脸G7691G7424G1328G1038GA的G2033G3999G12193G13688，G9994G2530该G2033G3999G12193G13688进G15904G13333G8554（交G2461和G2476G5334）。具G1319的G13333G8554过程是G4570人脸G7691G7424G2022分G1038一些具有一定G16833义的G4388G3371（如G11536G11567，G21775G4388等）G7481进G15904交G2461和G2476G5334；G4557于G2476G5334G12651G4388，G7424G7003G17836G18331用了人脸重G2164G1821等G6228G7427，用G7481G1028G4512G7691G7424G19610G2524的多G7691性。这G7691经过G13333G8554G993G7041G10995G6116G7044的G2530G1207，G1038了G4466G10628G2530G1207的G256G1260G14000G2167G8772G257，G7424G7003在G13333G8554的过程中G18331用分类G3132SNOW（SPARSENETWORKOFWINNOW）G7481G4557G7044G10995G6116的G2530G1207进G15904G16792价。G8611G8437经过SNOWG16792价G2530G11053G991G7481的G16311和G2033G3999G12193G13688一G17227G7512G6116G991一G1207的G10250G1207，进G15904G7044一G17730的G13333G8554。G1038了配G2524人脸G7691G7424G19610G2524的迭G1207G1260G2282，该分类G3132SNOWG8611经过一G8437GA迭G1207都用G2033G3999G12193G13688和上一G1207中经过SNOWG16792价G2530G11053G991G7481的G2530G1207G7481重G7044G16769G13463，再用G7044G16769G13463得到的分类G3132G16792价G991一G1207的G16311。G1038了确定GA迭G1207的终止条件，在GA迭G1207过程中G8611G1207G16769G13463的分类G3132SNOW都在校验G19610上进G15904测试，并比G17751得到的G13479果，当相连几G1207G16769G13463的分类G3132性能差G5334缩小到预G1820指定的一G1022阈值时即可停止迭G1207。G4466验G15932明GA可G1209在G13333G855440G1207G2530终止，G16769G13463G19610G2524在数G18339上也得到了大幅度的G6205G5364，而且数G18339G1260G2282的G16769G13463G19610G2524可G1209显著G3332G6564高分类G3132性能。G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003II2提出了基于流形的训练集分布优化方法GAG13333G8554会使得G2530G1207的人脸G7691G7424数G18339急剧膨胀，G1038了控制G2530G1207的G16280G8181，需要G4557其进G15904G991G18331G7691；同时GAG10995G6116的G2530G1207G7691G7424G19610的分G5079也需要G1260G2282。由于人脸数据分G5079的G19762线性，G7424G7003G18331用流形MANIFOLD的方法G7481G4557GAG10995G6116的G2530G1207G7691G7424G19610G2524进G15904G991G18331G7691和分G5079G1260G2282。其大致的思想是G1820利用ISOMAP（ISOMETRICFEATUREMAPPING）G4557GAG13333G8554的G2530G1207进G15904流形学习，并根据G7691G7424G4557在高维流形空间中的测G3332距离G4557数据G19610中过于密G19610的G3332方进G15904稀疏G2282G991G18331G7691，G1209删除GA产G10995的冗余G7691G7424。G9994G2530，基于ISOMAP学习得到的数据G19610低维流形嵌入，利用LLE（LOCALLINEAREMBEDDING）G12651法G4557该嵌入中G17751大的空洞进G15904插值，从而得到一G1022分G5079更G1038G2524理的数据流形。G4466验G15932明，G3252G1038人脸数据的G19762线性，基于流形G1260G2282G16769G13463G19610分G5079的方法取得了G17751好的效果。3提出了基于支持向量机的训练集边界分布优化方法G17902过GA和流形等重G18331G7691可G1209G4557G6922G19610的人脸G7691G7424G19610G2524进G15904数G18339上的G6205充和分G5079上的G1260G2282。但是G4557于分类边界上的人脸G7691G7424，由于其适应度值G17751低，G3252而容易在GA迭G1207的过程中被抛弃。但是支持向G18339机（SVMSUPPORTVECTORMACHINE）的理论G15932明，分G5079在类别边界上的G7691G7424即支持向G18339，会在基于边界的分类G3132学习G12651法中发挥重要G1328用。G3252G8504，G1038了进一步G1260G2282G17902过GA和流形方法重G18331G7691的人脸G7691G7424G19610G2524的分G5079，G17836需要G4557人脸G7691G7424G19610的边界进G15904G1260G2282，即织补上那些位于分类边界上的G7691G7424，G1209使得G6922G19610的人脸和G19762人脸G19610G2524的边缘更G2164清晰。G1038G8504，G7424G7003G6564G1998了一G12193基于SVM的、G19762线性的G16769G13463G19610边界分G5079G1260G2282G12651法，即嵌入图像欧式距离的G12946简G19610方法。该G12651法G17902过产G10995边界上的虚拟G7691G7424G7481G4557人脸和G19762人脸的边界处缺失的G7691G7424进G15904织补，G1209G1260G2282人脸和G19762人脸G19610G2524中位于边界附近的G7691G7424分G5079。G4466验G15932明，G18331用SVMG1260G2282G7691G7424G19610G2524的边界分G5079G4557G6564高分类G3132的性能效果明显。尽管基于G1260G2282的G7691G7424G19610G2524G16769G13463得到的ADABOOST分类G3132已经取得了G19762常好的人脸检测效果，但是在人脸检测的过程中，仍G9994存在一些误检。G1038了进一步改善人脸检测系统的性能，G7424G7003G17836G6564G1998了韦伯局部描述G4388（WLDWEBERLOCALDESCRIPTOR）用于学习一G1022基于SVM的人脸检测校验G3132，并G4570其添G2164在ADABOOST分类G3132的G2530端，可G1209G4557输入图像中ADABOOST判G1038人脸的G4388窗口进G15904过滤，从而G6564高G19610G6116G2530的分类G3132性能。基于G7424论G7003G6564G1998的方法G1260G2282的G7691G7424G19610G16769G13463的人脸检测系统已经获得了G4466际G6700G16213III应用，并取得了G17751好的效果。而且，尽管G7424G7003G1209人脸检测中的G7691G7424G1260G2282G1038例，G4466际上基于统计学习方法的性能大多依赖于G6922G19610的G16769G13463G19610的数G18339和分G5079，G3252G8504G7424G7003的方法也可用于其他基于统计学习的G16769G13463G19610的G1260G2282中，G1209G6564高其他分类G3132的性能。关键词G16769G13463G19610G1260G2282；重G18331G7691G6228G7427；人脸检测；G17963G1268G12651法；流形；支持向G18339机；ADABOOST；韦伯定律G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003IVABSTRACTFACEDETECTIONISTODETERMINEWHETHERTHEREAREANYFACESWITHINAGIVENIMAGE,ANDRETURNTHELOCATIONANDEXTENTOFEACHFACEINTHEIMAGEIFONEORMOREFACESAREPRESENTOVERTHEPASTTENYEARS,FACEDETECTIONHASBEENTHOROUGHLYSTUDIEDINCOMPUTERVISIONSOCIETYFORITSFUNDAMENTALCHALLENGEANDINTERESTINGAPPLICATIONS,SUCHASVIDEOSURVEILLANCE,HUMANCOMPUTERINTERACTION,FACERECOGNITION,ANDFACEIMAGEDATABASEMANAGEMENTETCWHILETHELEARNINGBASEDMETHODSHAVEACHIEVEDGREATSUCCESSFORFACEDETECTIONINTHEPASTTHREEDECADES,THEPERFORMANCEOFTHESELEARNINGBASEDMETHODSSTILLHIGHLYDEPENDSONTHECOLLECTEDTRAININGSETTHEREFORE,THEYALLSUFFERFROMACOMMONPROBLEMOFDATACOLLECTIONFORTRAININGGENERALLYSPEAKING,TOACHIEVEACCEPTABLEDETECTIONACCURACY,ONEHASTOLABORIOUSLYCOLLECTMANYSUFFICIENTREPRESENTATIVEFACEANDNONFACEEXAMPLES/SAMPLESHOWEVER,WENOTICETHAT,COMPAREDWITHTHEGREATDEALOFEFFORTSONCLASSIFIERDESIGNWHENGIVENASTATICTRAININGSET,VERYLITTLEATTENTIONHASBEENPAIDTOTHEOPTIMIZATIONOFTHETRAININGSETESPECIALLYFORTHEFACESAMPLESINTHISTHESIS,WEPROPOSEAMETHODTOOPTIMIZETHECOLLECTEDTRAININGSETTOIMPROVETHEPERFORMANCEOFATRAINEDCLASSIFIER,ANDFACILICIATETHECOLLECTIONOFTHETRAININGSETSPEFICIALLY,THEOPTIMIZATIONOFTHETRAININGSETISCOMPOSEDOFTWOPARTSONEISTOEXPANDTHETRAININGSETANDADDINGVARIATIONSONTHEPOSITIVESAMPLESTHEOTHERISTOOPTIMIZEITSDISTRIBUTIONTHEMAINCONTRIBUTIONOFTHISTHESISINCLUDES1EXPANDINGTHETRAININGSETBYGAWEFIRSTLYPRESENTAGENETICALGORITHMGABASEDMETHODTOSWELLFACEDATABASETHROUGHEXPANDINGTHEEXISTINGFACESSPEFICIALLY,WEMANUALLYCOLLECTATRAININGSETANDUSEITASANINITIALPOPULATIONWHICHISTHENFORREPRODUCTIONCROSSOVERANDMUTATIONTHECROSSOVERANDMUTATIONOPERATORISDESIGNEDINTERMSOFTHEPARTICULARITIESOFFACESFORTHEMUTATIONOPERATOR,WEALSOUSETHERELIGHTINGMETHODTOENRICHTHEDIVERSITIESOFTHETRAININGSETTHESEGENERATEDOFFSPRINGSARETHENEVALUATEDBYACLASSIFIEROFSNOWSPARSENETWORKOFWINNOWTHOSESURVIVALSANDTHEINITIALPOPULATIONCONSISTOFTHESOLUTIONSOFTHECURRENTGENERATIONANDTHENAREG6700G16213VUSEDTOREPRODUCETHENEXTGENERATIONTHEINTIALPOPULATIONANDTHESOLUTIONSOFTHELATESTGENERATIONAREALSOUSEDTORETRAINTHECLASSIFIERSNOWTOFOLLOWTHEOPTIMIZATIONOFTHETRAININGSETDURINGTHEGAITERATIONSTHERETRAINEDSNOWISTHENUSEDTOEVALUATETHESOLUTIONSOFTHENEXTGENERATIONITISALSOTESTEDONAVALIDATIONSETANDTHETESTEDRESULTISCOMPAREDTOTHENEIGHBORGENERATIONSWHENTHEPERFORMANCEDIFFERENCEOFSNOWBETWEENTHENEIGHBORGENERATIONSISSMALLERTHANAGIVENTHRESHOLD,THEGAITERATIONISSTOPPED2OPTIMIZATIONOFTHESAMPLESBYMANIFOLDTHEREPRODUCTIONOFGAEXPANDSTHETRAININGSETQUICKLYTOCONTROLTHESIZEOFTHEGENERATION,THEEXPANDEDPOPULATIONSNEEDTOBESUBSAMPLEDANDTHEIRDISTRIBUTIONSALSONEEDTOBEOPTIMIZEDDUETOTHENONLINEARITYOFTHEFACESAMPLES,WEUSETHEMANIFOLDTOCOMPLETETHEGOALSSPECIFICALLY,WEUSEISOMAPTOLEARNTHEMANIFOLDOFTHEGAPOPULATIONSAFTERCOMPUTINGTHEGEODESICDISTANCESBETWEENSAMPLESINAHIGHDIMENSIONALSPACE,WESUBSAMPLETHEPOPULATIONSTOOBTAINAREPRESENTATIVETRAININGSETAFTERTHAT,WEEMBEDTHEFACESETTOALOWDIMENSIONALMANIFOLDSPACEANDOBTAINALOWDIMENSIONALEMBEDDINGSUBSEQUENTLYWEUSELLETOINTWEAVETHESUBSAMPLEDSOLUTIONSTORESHAPETHEDISTRIBUTIONOFTHEPOPULATIONSBYTHEOPTIMIZATIONUSINGMANIFOLDS,THEDISTRIBUTIONSOFTHEGENERATEDPOPULATIONSAREOPTIMIZED3OPTIMIZATIONOFTHESAMPLESLOCATINGTHEBOUNDARYBYSVMTHERESAMPLINGMETHODSGAMGAANDMANIFOLDSUBSAMPLETHEPOPULATIONSOFGAANDOPTIMIZETHEIRDISTRIBUTIONSHOWEVER,THOSEFACESAMPLESNEARTOTHECLASSBOUNDARYAREDISCARDEDDURINGTHEGAITERATIONSDUETOTHEIRRELEATIVELYLOWERFITNESSACCORDINGTOTHETHEORYOFSUPPORTVECTORMACHINESSVM,FORTHOSEGEOMETRICAPPROACHBASEDCLASSIFICATIONMETHODS,EXAMPLESCLOSETOTHEBOUNDARYUSUALLYAREMOREINFORMATIVETHANOTHERSTHUS,THOSEMISSINGSAMPLESNEARTOTHEBOUNDARYNEEDTOBEINTERPOLATEDTOOPTIMIZETHERESAMPLEDTRAININGSETBYGAMWEADDRESSTHEPROBLEMOFENHANCINGAGIVENTRAININGSETANDPRESENTANONLINEARMETHODTOTACKLETHEPROBLEMEFFECTIVELYBASEDONSVMANDIMPROVEDREDUCEDSETALGORITHMIRS,THEMETHODGENERATESNEWEXAMPLESLYINGCLOSETOTHEFACE/NONFACECLASSBOUNDARY,TOENLARGETHEORIGINALDATASETANDHENCEIMPROVEITSDISTRIBUTIONTHUS,THERESULTINGG2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003VISAMPLESETISOPTIMIZEDAFTERTHERESAMPLINGOFGAMMOREOVER,WEPROPOSEANEWDESCRIPTORWLDWEBERLOCALDESCRIPTORBASEDONTHEPERCEPTIONOFHUMANBEINGWETHENUSETHEFEATURESEXTRACTEDBYWLDTOTRAINANSVMBASEDCLASSIFIERANDUSEITASAPOSTDETECTORITISBECAUSETHATALTHOUGHTHEADABOOSTTRAINEDONTHEOPTIMIZEDTRAININGSETHASOBTAINEDAWELLPERFORMANCE,THEREARESTILLSOMEFALSEALARMSDURINGTHEDETECTIONTHESVMTRAINEDUSINGWLDFEATURESISTHENUSEDTOREDUCETHEFALSEALARMSBYTHEADABOOSTCLASSIFIERTOIMPROVETHEPERFORMANCEOFTHECONCATENATEDCLASSIFIERTHEOPTIMIZEDTRAININGSETHASBEENUSEDTOTRAINAFACEDETECTORSYSTEMANDTHESYSTEMHASBEENSUCCESSFULLYAPPLIEDINPRACTICEMOREOVER,WHILEMOSTOFTHERESEARCHERSPAYMUCHATTENTIONTOTHEDESIGNOFTHECLASSIFIERSANDRESAMPLINGBYBOOTSTRAP,FORINSTANCETHENEGATIVESAMPLES/NONFACES,THEEXPERIMENTALRESULTSOFTHISTHESISINDICATETHATOPTIMIZATIONOFTHETRAININGSETBYRESAMPLINGOFPOSITIVESAMPLES/FACESCANALSOIMPROVETHEPERFORMANCEOFACLASSIFIERSIGNIFICANTLYFURTHERMORE,THEPROPOSEDMETHODCANFACILITATETHECOLLECTIONOFAGOODTRAININGSETALTHOUGHTHEPROPOSEDMETHODISUSEDTORESAMPLEFACESINTHISTHESIS,WEBELIEVETHATTHEPROPOSEDMETHODCANALSOBEAPPLIEDTOLEARNOTHERCLASSIFIERSTOIMPROVETHEIRPERFORMANCESINCEMOSTOFTHESTATISTICALMETHODSHIGHLYDEPENDONTHECOLLECTIONOFTHETRAININGSETKEYWORDSOPTIMIZATIONRESAMPLINGFACEDETECTIONGENETICALGORITHMMANIFOLDSVMADABOOSTWEBERSSLAWG11458G5417VII目录摘要IABSTRACTIV第1章绪论111课G20076背景G2462研究意义112人脸检测的一般计G12651G8181G3423213人脸检测G8022述3131基于G16280则的G12651法4132基于统计学习的G12651法514G7691G7424重G18331G7691G8022述815G7691G7424重G18331G7691的G2172机8151G7691G7424重G18331G7691G8022述9152G7691G7424重G18331G7691的流程10153G12651法G2524理性分G75241116主要研究内容1417论G7003的G13464织G13479G751215第2章基于遗传算法的训练集扩张1721G5353G163401722G17963G1268G12651法简G118318221步G2060018222主要特G986920223应用2123G7691G7424的预处理2124基于GA的虚拟人脸G10995G6116G12651法22241G7691G7424的G13546G1173322242G2033G3999G12193G13688G7512G611622243G1022G1319G17885G6333G12651法23244交G246123245G2476G533424246适应度G16792价25G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003VIII247终止条件2525人脸G7691G7424重G2164G182125251基于G16855和图像的人脸G7691G7424重G2164G182125252G11458G7643G1821G10043的配置29253重G2164G1821的过程3126适应度G16792价32261SNOW分类G313232262分类G3132的特G546134263G16769G13463与G16792价3527基于GA的人脸G7691G7424G10995G6116G12651法的具G1319G4466G1062836271G7691G7424的G2022分36272G993同G4388G19610G7691G7424的GAG6817G132836273GA中G8022G10587的G17885G63333828G4466验G2462分G752439281基于GAG10995G6116的G7691G742439282GAG6817G1328G4557分类G3132性能的G5445G272140283关于G18331用GAG6205G5364G16769G13463G19610方法的G16764论4429小G1347945第3章基于流形的训练集分布优化4731G5353G163404732基于ISOMAP的G991G18331G7691G12651法47321G991G18331G7691的G2172机47322改进的ISOMAPG12651法49323过度密G19610G2318域的G991G18331G76915133基于LLE的G7691G7424插值52331插值的G2172机53332过度稀疏G2318域的插值5434G4466验G2462分G752456341基于GAMG1260G2282G16769G13463G19610的G4466G10628G13466G1442256342G7691G7424G19610G991G18331G7691G4557分类G3132性能的G5445G272158343流形插值G4557分类G3132性能的G5445G272161344GAMG1260G2282的G16769G13463G19610的G6524广性验G1678963345G18331用小G7691G7424G19610G1328G1038GAMG2033G3999G12193G13688的G12651法性能验G1678967G11458G5417IX35小G1347969第4章基于SVM的训练集边界分布优化7041G5353G163407042基于SVMG4466G10628边缘G7691G7424的插值70421边缘G7691G7424插值的G2172机70422G12946简G19610G12651法71423基于改进的G12946简G19610G12651法的插值方法7343G4466验G2462分G752474431G993同图像距离度G18339嵌入G12946简G19610G12651法的比G1775175432基于SVM插值G4557分类G3132性能的G5445G27217844小G1347979第5章人脸检测原型系统及其性能改善8051G5353G163408052人脸检测G2419G3423系统8053基于韦伯局部描述G4388的系统性能G3698G539083531G2081人的G5049G132883532韦伯定律83533WLDG12651G438884534WLD的特G986989535WLDG12651G4388描述能G2159的改进9054G4466验G2462分G752492541基于WLD的特G5461G2462分类G3132的G16769G13463与测试92542系统性能测试97543和其他方法的性能比7424G7003G12651法G4557分类G3132性能的G5445G2721小G1347910155小G13479104结论106参考文献110攻读学位期间发表的学术论文119哈尔滨工业大学博士学位论文原创性声明121哈尔滨工业大学博士学位论文使用授权书121致谢122G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003XCONTENTSCHINESEABSTRACT1ENGLISHABSTRACT1CHAPTER1INTRODUCTION111BACKGROUND112AGENERALMODEFORFACE213ABRIEFSUMMARYFORFACEDETECTION3131MODALBASEDALGORITHM4132STATISTICSBASEDALGORITHM514ABRIEFINTRODUCTIONTORESAMPLING815MOTIVATIONFORRESAMPLING8151REASONS9152FLOWCHART10153REASONABILITYANALYSIS1116MAINCONTRIBUTION1417STRUCTUREOFTHETHESIS15CHAPTER2EXPANDINGSAMPLESBASEDONGA1721INTRODUCTION1722ABRIEFINTRODUCTIONTOGA18221PROCESS18222CHARACTERISTICS20223APPLICATIONS2123SAMPLEPREPROCESSING2124GENERATINGVIRTUALFACESBASEDONGA22241SAMPLEENCODING22242INITIALPOPULATION22243SELECTION23244CROSSOVER23245MUTATION24246EVALUATION25G11458G5417XI247STOPCRITERIA2525FACERELIGHTING25251FACERELIGHTINGBASEDONHARMONICIMAGES25252CONFIGURATIONOFTHETARGETLIGHTING29253FLOWCHARTFORRELIGHTING3126FITNESSEVALUATION32261ABRIEFOVERVIEWOFSNOW32262FEATURESFORSNOW34263TRAININGANDEVALUATION3527IMPLEMENTATIONOFGENERATINGFACESBASEDONGA36271SAMPLESDIVISION36272GAOPERATIONSFORDIFFERENTSUBSETS36273PROBABILITYFORCROSSOVERANDMUTATION3828EXPERIMENTALRESULTS39281GENERATINGSAMPLESBASEDONGA39282PERFORMANCEVARIATIONSBYGA40283DISCUSSION4629SUMMARY45CHAPTER3OPTIMIZATIONOFTRAININGSETBYMANIFOLD4731INTRODUCTION4732SUBSAMPLINGBASEDONTHEIMPROVEDISOMAP47321MOTIVATION47322IMPROVEDISOMAP49323SUBSAMPLINGBYISOMAP5133INTERWEAVINGBASEDONLLE52331MOTIVATION53332INTERWEAVING5434EXPERIMENTALRESULTS56341RESAMPLINGBASEDONGAM56342PERFORMANCEIMPROVEMENTBYSUBSAMPLING58343PERFORMANCEIMPROVEMENTBYINTERWEAVING61344GENERALIZATIONVERIFICATIONOFRESAMPLINGBASEDONGAM63345ASMALLINITIALPOPULATIONFORGAM67G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003XII35SUMMARY69CHAPTER4OPTIMIZATIONOFTRAININGSETBYSVM7041INTRODUCTION7042MENDINGTHEBOUNDARYSAMPLESBASEDONSVM70421MOTIVATION70422REDUCINGSETMETHOD71423MENDINGBASEDONIRS7343EXPERIMENTALRESULTS74431COMPARISONOFDIFFERENTDISTANCE75432MENDINGBYSVM7844SUMMARY79CHAPTER5APROTOTYPESYSTEMANDITSPERFORMANCEIMPROVEMENT8051INTRODUCTION8052APROTOTYPESYSTEMOFFACEDETECTION8053WLDFORTHEPERFORMANCEIMPROVEMENTOFTHESYSTEM83531PERVIOUSWORK83532WEBERLAW83533WLD84534PROPERTIESOFWLD89535IMPROVEMENTONWLD9054EXPERIMENTALRESULTS92541CLASSIFIERTRAINEDUSINGWLD92542APPLICATIONTEST97543PERFORMANCECOMPARISONWITHTHESTATEOFTHEARTMETHOD100544ASUMMARYOFPERFORMANCECOMPARISONONTHEPROPOSEDMETHODS10155SUMMARY104CONCLUSION106REFERENCE110PUBLICATION119STATEMENTOFCOPYRIGHT121LETTEROFAUTHORIZATION121ACKNOWLEDGEMENT122G11458G5417XIII图表目录图11人脸检测的G12046例1图12G12651法流程图10图13G18331用G7424G7003G6564G1998的G12651法G1260G2282人脸G19610的G12046意图11图14G6922G19610的可能的G2033G3999G12193G13688G12046意图12图15利用G7424G7003G6564G1998的G12651法G4557G6922G19610的数据G19610G2524G6205充的G12046意图13图21利用GAG10995G6116虚拟人脸流程图17图22G2476G5334与交G2461G12046意图20图23人脸G7691G7424的交G2461和G2476G5334G12651G4388G12046意图23图24人脸G7691G7424的重G2164G1821G12046意图28图25G11458G7643G1821G10043的配置G12586G1106530图26在G11458G7643G1821G10043条件G991的G4466际人脸图像和G1284计的相应G1821G1004330图27人脸G7691G7424重G2164G1821的G5049G1328流程31图28SNOW分类G3132的G1319系G13479G751233图29G9796度特G5461的G18339G2282方G770835图210GAG6817G1328过程的G12046意图37图211GAG10995G6116的一些G7691G7424G12046例40图212基于GAG993同G1207的G16311的分类G3132在校验G19610上测试得到的ROCG7366线41图213基于GA迭G1207得到的G16311G16769G13463SNOW得到的ROCG7366线图42图214G993同的G7691G7424G16792价方法性能比G1775144图31G10802G3775G2379流形G12046意图48图32基于测G3332距离进G15904G991G18331G7691的G12046意图48图33改进的ISOMAPG12651法51图34基于改进的ISOMAP的G991G18331G7691G12651法52图35低维流形嵌入中进G15904插值的G12046意图53图36虚拟G7691G7424的重G7512G12046意图55图37基于LLEG3647充低维流形嵌入中的稀疏G2318域56图38ISOMAPG991G18331G7691G4557G12651法性能的G5445G272159图39三G12193G993同的G991G18331G7691G12651法G4557GA中间G16311G991G18331G7691的ROCG7366线图60图310G18331用LLE插值流形G4557G12651法性能的G5445G272161图311G18331用G993同的G12651法G7512G17908虚拟G7691G7424的G12651法性能比G1775162G2716G4584G9404G5049G1006G3835G4410G5049G4410G2350G3775G4410G1313G16782G7003XIV图312G12227分图G1815G13044值计G1265163图313检测窗内的G11709形特G5461例G438864图314ADABOOSTG12651法学习的大致流程65图315分G13435分类G3132检测过程的G12046意图65图316G18331用GAM40得到的G993同G16280G8181的G16311G19610的分类G3132在MITCMU的G8503G19766人脸测试G19610G2524上测试得到的ROCG7366线图66图317G18331用小G7691G7424G19610G1328G1038GAM的G2033G3999G12193G13688得到的ROCG7366线图68图41基于SVM插值的系统G13479G7512G7706图71图42近G1296误差G19555产G10995的G12946简G19610向G18339数G18339G3698G2164而G991G19489的G7366线G4557比图76图43三G12193G12651法产G10995G12946简G19610向G18339所进G15904的迭G1207G8437数G4557比76图44类别边界上的支持向G18339与IMEDRSVG12651法G10995G6116的G12946简G19610向G1833977图45G993同数G11458的G12946简G7691G7424的重G7512误差G7366线78图46G2164入G12946简G19610G7691G7424G2530分类G3132的G12651法性能G2476G228279图51人脸检测系统G7706图81图52人脸检测系统界G19766G6142图82图53计G12651WLD的G12046意图84图54G2465G8503G2011G2001数YARCTANXG7366线图86图55A一幅G14270G9994G3342景图像,B该G14270G9994图像中G9796度值G7811度的分G507986图56滤G8886图像G5414一G2282到0,255G2530的比G17751G12046意图87图57如G1321从一幅图像中G6564取WLDG11464方图的G12046意图89图58G12544一G15904G15932G12046G2419G3999图像，G12544G1120G15904G15932G12046WLD滤G8886G2530的差G5334G9620G2181图像90图59G18331用WLDG11464方图特G5461G7481描述人脸G7691G7424的G12046意图92图510G993同G2454数取值G4557WLD描述能G2159的G5445G272194图511基于WLD和LBPG16769G13463的分类G3132在MITCMU人脸测试G19610上的性能比G1775196图512一些在MITCMUG8503G19766人脸测试G19610G2524上检测的G13479果96图513基于WLDG4557人脸检测中G1955G4581误检的能G215997图514G6564G1998G12651法得到的分类G3132和G10628有的G12651