人脸识别的简单算法.docx

人脸识别的简单算法.docx

《人脸识别的简单算法.docx》由会员分享，可在线阅读，更多相关《人脸识别的简单算法.docx（14页珍藏版）》请在冰豆网上搜索。

人脸识别的简单算法

Rowley-Baluja-KanadeFaceDetector

Author:

ScottSanner

Contents

∙Introduction

∙Algorithm

∙DataPreparation

∙Training

∙ImageScanning

∙Testing

∙Conclusion

∙References

∙Software

Introduction

ThegoalofthisprojectistoimplementandanalyzetheRowley-Baluja-Kanadeneuralnetfacedetectorasdescribedin[2]alongwithsomeenhancementsfortrainingandrecognitionproposedbySungandPoggioasdescribedin[3].Thebasicgoalunderlyingbothapproachesistotrainaneuralnetworkorotherrecognitionsystemonalabelleddatabaseoffaceandnon-faceimages.Thisfaceclassifiercanthenbeusedtoscanoveranimageresolutionpyramidtodeterminethelocationsandscalingofanyfaces（ifpresent）andreturnthemtotheuser.

Overall,thetaskoffacerecognitioncanbeextremelydifficultgiventhewidevarietyoffacestomatch,thepresenceoffacialhair,variationsinlightingandshadowing,andthepossibilityofangular,scaling,anddimensionalvariances.Consequentlyanidealfacedetectorshouldattempttomitigatealloftheseproblemswhileachievingahighdetectionrateandminimizingthenumberoffalsepositives.Aswewillseeinthelatterrequirement,thereisatradeoffbetweenthepositivedetectionrateandthefalsepositiverateandthebalancebetweenthetwowillneedtobeevaluatedbytheindividualuserandapplicationdomain.

AlgorithmOverview

Toachievetheabovegoalsforfacedetection,weuseageneralalgorithmthatisastraightforwardapplicationofdatapreparation,training,andimagescanning.Thisalgorithmisoutlinedbelow:

NormalizeTrainingData:

-Foreachfaceandnon-faceimage:

-Subtractoutanapproximationoftheshadingplane

tocorrectforsinglelightsourceeffects

-Rescalehistogramsothateveryimagehasthesame

samegraylevelrange

-Aggregatedataintolabeleddatasets

TrainNeuralNet:

-UntiltheNeuralNetreachesconvergence（oradecrease

inperformanceonthevalidationset）:

-Performgradientdescenterrorbackpropagationon

ontheneuralnetforthebatchofalltrainingdata

ApplyFaceDetectortoImage:

-Buildaresolutionpyramidoftheimagebysuccessively

successivelydecreasingtheimageresolutionateach

levelofthepyramid,stoppingatsomedefaultminimum

resolution

-Foreachlevelofthepyramid

-Scanovertheimage,applyingthetrainedneuralnet

facedetectortoeachrectanglewithintheimage

-Ifapositivefaceclassificationisfoundfora

rectangle,scalethisrectangletothesize

appropriatefortheoriginalimageandadditto

thefacebounding-boxset

-Returntherectanglesinthefacebounding-boxset

DataPreparation

Inperformingfacedetectionwithaneuralnet,afewface-specificandnon-face-specificissuesarise.

Intherealmoffacespecificissues,wedonotwantthebackgroundtobecomeinvolvedinfacematching.Consequently,ifpersonAisintwodifferentsettingswewanttoensurethatweperformaswellaspossibleindetectingpersonA'sfacedespitethebackgroundvariation.Ifwewereonlytolookatpotentialcandidaterectanglesforafacethenwewouldreceiveinterferencefromthecornerswhicharemorelikelytoconsistofbackgroundthanfacepixels.Neuralnetsareespeciallysusceptibletosucherrorssinceanyconsistenciesbetweendatainthetrainingset（nomatterhowplausibleapredictorofface-hoodinreallife）willlikelybedetectedandexploited.Thus,as[3]suggests,itisagoodideatomaskanovalwithinthefacerectangletoprunethepixelsusedintraininginneuralnet.Fortruefaceimages,thisusuallyguaranteesthatonlypixelsfromthefaceareusedasinputtotheneuralnet.Forourimplementation,weusetheovalmaskwhichcanbeseeninfigure3.Theboundingrectangleforthismaskis18x27pixels.

Anotherfacespecificissueisthatofposeorglasses.Wewanttorecognizeafaceinvariantofwhetherapersonissmiling,sad,wearingglasses,ornotwearingglasses.Consequentlyitisimportanttoconstructasetoftrainingdatawhichcoversabroadrangeofhumanemotions,poses,andglasses/non-glasseswearingfaces.Thisensuresthegreatestgeneralizationwhenapplyingthefacedetectortofaceswhichhavenotbeenseenbefore.Forourdataset,weuse30facesandtheirleft-rightflippedversionswithavarietyofemotionsandposesascontainedintheYaleFaceDatabase[1].Itwouldbeadvantageoustohavemorefacesandposesthanthisbutthetimelimitsofthisprojectconstrainedtheamountoftimethatcouldbedevotedtophotoediting（sincetheYaleFaceDatabaseisnotinadirectlyusableformat）.

Onenon-facespecificissueisthatoflightingdirection.Neuralnetsareespeciallysusceptibletopixelmagnitudevaluesandthedifferencesbetweenimagesilluminatedfromtheleftorrightmaybeenoughtomakethemappearastwodifferentclassificationsfromtheperspectiveoftheneuralnet.Consequently,therehastobesomemethodforcorrectingforunidirectionallightingeffects（evenifonlyapproximate）.Additionally,notallimageswillhavethesamegrayleveldistributionorrangeanditisimportanttomitigatethisasmuchaspossibletoavoidbiaseffectsduetograyleveldistribution.

Forourdataset,weattempttocorrectforunidirectionallightingeffectsassuggestedby[2]byfittingasinglelinearplanetotheimage.Thisplanecanbecomputedefficientlythroughsimplelinearprojectionsolvingtheequation[XY1]*C=Z（whereX,Y,andZarethevectorscorrespondingtotheirrespectivecoordinatevalues,1isavectorof1'stocomputetheconstantoffset,andCisavectorofthreenumbersdefiningthelinearslopesintheXandYdirectionsandtheconstantoffset）.TocomputeC,wesimplyneedtocompute（[XYO]'*[XYO]）^-1*[XYO]'*Z.TheseplanecoefficientsinCapproximatetheaveragegraylevelacrosstheimageunderalinearconstraintandthuscanbeusedtoconstructashadingplanethatcanbesubtractedoutoftheoriginalimage.Oncethelightingdirectioniscorrectedfor,thegrayscalehistogramcanthenberescaledtospantheminandmaximumgrayscalelevelsallowedbytherepresentation.

Thiswasdoneforourface（andnon-face）trainingdataandanoriginalsubsetofimagesareshowninfigure1below:

Figure1:

InitialImages.

Fromfigure1,wethenapproximatetheshadingplaneasshownbelow.Notethatthesecondandthirdimagesinfigure1showheavydirectionallightingeffectsandthattheshadingplaneinfigure2accuratelyrepresentstheseeffects.

Figure2:

ShadingApproximations.

Now,giventheimagesinfigures1and2,wecansubtractfigure2fromfigure1andrescalethegraylevelstotheminimumandmaximumrangeforourrepresentation.Wecanthenapplyamasktothisimagetoremovebackgroundinterference.Thisresultisshownbelowinfigure3.

Noteinthefollowingfigurethattheunidirectionallightingeffectspresentintheoriginalsecondandthirdimages（figure1）havenowbeenremovedandthatunlikefigure1,allimagesinfigure3haveapproximatelythesamegrayleveldistribution.Thisnormalizationisextremelyimportanttoproperfunctioningoftheneuralnetwork.

Figure3:

NormalizedandMaskedImages.

Inadditiontothefaceimages,wealsoperformthesamenormalizationonasetofnon-facesceneryimages.Sincewenormalizeallimagesduringthefacedetectionscanningprocess,itisimportanttotrainonnormalizedsceneryimagessincetheunnormalizedsetwouldbeunrepresentativeofthoseseenduringtraining.Asetoffiveofthe160sceneryimagesisshownbelowinfigure4.（Actuallyonly40sceneryimageswereused,buttheirleft-rightandupside-downversionswerealsoaddedtothedataset.）

Figure4:

Non-faceImageExamples.

Onceallofthetrainingdataimageshavebeennormalizedtheyareaggregatedintolabelleddatasetsandpassedontothetrainingphase.Additionally,thenormalizationprocessoccursoncemoreduringtheactualfacedetectionprocess,i.e.allimagesrectanglesarenormalizedbeforeclassifyingthemwiththeneuralnet.

Training

Givenourmasksize,weuseaneuralnet（createdandtrainedusingMatlab'sneuralnettoolbox）withapproximately400inputunitsconnecteddirectlytoacorrespondingpixelwithintheimagemask,20hiddenunits,and1outputunitusedforprediction（yieldingidealtrainingvaluesof-0.9forsceneryand0.9foraface）.

Theneuralnetistrainedfor500epochs（oruntilerrorincreasesonanindependentvalidationchosenseparatelyfromthetrainingset）.Thesumofsquareserrorrateonthetrainingset（blue）andthevalidationset（red）areplottedbelowinFigure5.Notethataroundepoch50,thevalidationseterrorsurpassesthetrainingseterror（aswouldbeexpected）.Howeverthevalidationseterrorneverincreasesfromaprevioustimestepandthereforethenetworkprocedestoapproximateconvergence.Thisindicatesthatinsomesensethetrainingsetisadequateenoughtogeneralizetounseeninstances.

Figure5:

TrainingErrorvs.Epochs.

Thefinalperformanceofthenetworkonallofthefaceandnon-facedataisshownbelowintable1.Thenetworkapparentlyperformsmuchbetteratdetectingnon-faceswhichisprobablyduetothebiastowardnon-facetrainingimagesinthedataset.However,thishastheadvantageofyieldingalowerfalsepositiveratethanifthebiashadbeeninfavorofthefaceimagesinstead.

FaceDetectionRate

Non-faceDetectionRate

OverallClassifcationRate

PercentageCorrect

86.7%

98.1%

97.7%

TrainingSetSize

60

160

220

Table1:

TrainingResults.

Nowthattheneuralnethasbeensuccessfullytrained,itcannowbeusedforclassifyingcandidatefacerectanglespasse