人脸识别外文文献Word文档下载推荐.docx

人脸识别外文文献Word文档下载推荐.docx

《人脸识别外文文献Word文档下载推荐.docx》由会员分享，可在线阅读，更多相关《人脸识别外文文献Word文档下载推荐.docx（10页珍藏版）》请在冰点文库上搜索。

slicenseandpassportcheck,bankingandcustoms

controlsystem,andotherfields[1].

Themainmethodsoffacerecognitiontechnologycanbesummeduptothreekinds:

basedongeometricfeatures,templateandmodelseparately.ThePCAfacerecognitionmethodbasedonK-Ltransformhasbeenconcernedsincethe1990s.Itissimple,fast.andeasytouse.Itcanreflectthepersonface'

scharacteristiconthewhole.Therefore,applyingPCAmethodinthefacerecognitionisunceasinglyimproving.

D.-S.Huangetal.（Eds.）:

ICIC2008,LNCS5226,pp.561–568,

2008.©

Springer-VerlagBerlinHeidelberg2008

ThispaperputsforwardamethodoffacerecognitionbasedonRed-BlackwavelettransformandPCA.Firstly,usingtheimprovedimagehistogramequalization[2]todoimagepreprocessing,eliminatingtheimpactofthedifferencesinlightintensity.Sec-ondly,usingtheRed-Blackwavelettransformtowithdrawthebluesub-bandoftherelativestablefaceimagetoobscuretheimpactsofexpressionsandpostures.Then,usingPCAtowithdrawthefeaturecomponentanddorecognition.ComparingwiththetraditionalPCAmethods,thisonecanobviouslyreducecomputationalcomplexityandincreasetherecognitionrateandanti-noiseperformance.Theexperimentalresultsshowthatthismethodmentionedinthispaperismoreaccurateandeffective.

2Red-BlackWaveletTransform

Liftingwavelettransformisaneffectivewavelettransformwhichdevelopedrapidlytheseyears.ItdiscardsthecomplexmathematicalconceptsandthetelescopicandtranslationoftheFouriertransformanalysisintheclassicalwavelettransform.Itde-velopsfromthethoughtoftheclassicalwavelettransformmulti-resolutionanalysis.Red-blackwavelettransform[3-4]isatwo-dimensionalliftingwavelettransform[5-6],itcontainshorizontal/verticallifinganddiagonallifting.Thespecificprinciplesareasbellow.

2.1Horizontal/VerticalLifting

AsFig.1shows,horizontal/verticalliftingisdividedintothreesteps:

1.Decomposition:

Theoriginalimagebyhorizontalandverticaldirectionisdividedintoredandblackblockinacross-blockway.

2.Prediction:

Carryonthepredictionusinghorizontaland

the

verticaldirectionfourneighborhood'

sredblockstoobtainablackblockpredictedvalue.

Then,usingthedifferenceoftheblackblockactualvalueandthepredictedvaluetosubstitutetheblackblockactualvalue.Itsresultobtainstheoriginalimagewaveletcoefficient.AsFig.1（b）shows:

f（i,j）¬

f（i,j）-[f（i-1,j）+f（i,j-1）+f（i,j+1）+f（i+1,j）]/4

（imod2¹

jmod2）



（1）

3.Revision:

Usingthehorizontalandverticaldirectionfourneighborhood'

sblackblock'

swaveletcoefficienttorevisetheredblockactualvaluetoobtaintheapproximatesignal.AsFig.1（c）shows:

f（i,j）+[f（i-1,j）+f（i,j-1）+f（i,j+1）+f（i+1,j）]/8

（imod2=



（2）

Inthisway,theredblockcorrespondstotheapproximatinginformationoftheimage,andtheblackblockcorrespondstothedetailsoftheimage.

2.2DiagonalLifting

Onthebasisofhorizontal/verticallifting,wedothediagonallifting.AsFig.2shows,itisalsodividedintothreesteps:

Fig.2.Diagonallifting

Afterhorizontal/verticallifting,dividingtheobtainedred blockintotheblueblockandtheyellowblockinthediagonalcrossway.

Usingfouroppositeangleneighborhood'

sblueblocktopredict adatainordertoobtaintheyellowblockpredictedvalue.Thenthedifferenceoftheyellowblockactualvalueandthepredictedvaluesubstitutestheyellowblockactualvalue.Itsresultobtainstheoriginalimagewaveletcoefficientofthediagonaldirection.AsFig.2（b）shows:

f（i,j）-[f（i-1,j-1）+f（i-1,j+1）+f（i+1,j-1）+f（i+1,j+1）]/4

（imod2=1,jmod2=1）

（3）

Usingfouroppositeangleneighborhoodyellowblockwaveletco-efficienttorevisetheblueblockactualvalueinordertoobtaintheapproximatesignal.AsFig.2（c）shows:

f（i,j）-[f（i-1,j-1）+f（i-1,j+1）+f（i+1,j-1）+f（i+1,j+1）]/8

（imod2=0,jmod2=0）

（4）

Afterthesecondlifting,thered-blackwavelettransformisrealized.

AccordingtotheEquations,itcananalyzesomecorrespondingrelationsbetweenthered-blackwavelettransformandtheclassicalwavelettransform:

namely,theblueblockisequaltothesub-bandLLoftheclassicaltensorproductwavelets,theyellowblockisequaltosub-bandHHandtheblackblockisequaltosub-bandHLandLH.Experimentalresultsshowthatitdiscardsthecomplexmathematicalconceptsandequations.Therelativityofimagecanmostlybeeliminatedandthesparserrepresen-tationofimagecanbeobtainedbytheRed-Blackwavelettransform.

TheimageafterRed-Blackwavelettransformisshowedinthe

Fig.3（b）,ontheleftcorneristhebluesub-bandblockimagewhichistheapproximateimageoforiginalimage.

Fig.3.Theresultofred-blackwavelettransform

3FeatureExtractionBasedonPCA[7]

PCAisamethodwhichanalysesdatainstatisticalway.Thismethoddiscoversgroupofvectorsinthedataspace.Usingthesevectorstoexpressthedatavarianceasfaraspossible.PuttingthedatafromtheP-dimensionalspacedowntoM-dimensionalspace（P>

>

M）.PCA use K-L transform to obtain the minimum-dimensional imagerecogni-tionspaceoftheapproximatingimagespace.Itviewsthefaceimageasahigh-dimensionalvector.Thehigh-dimensionalvectoriscomposedofeachpixel.Thenthehigh-dimensionalinformationspace maps the low-dimensional characteristic subspace by K-Ltransform. It obtains a group of orthogonal bases through high-dimensionalfaceimagespaceK-Ltransform.Thepartialretentionoforthogonalbasescreatesthelow-dimensionalsubspace.Theorthogonalbasesreservediscalled “Principlecomponent”.Sincetheimagecorrespondingtotheorthogonalbasesjustlikeface,sothisisalsocalled“Eigenfaces”method.Thearithmeticoffeatureextractionarespecifiedasfollows:

Forafaceimageofm×

n,connectingitseachrowwillconstitutearowvectorwhichhasD=m×

ndimensions.TheDisthefaceimagedimensions.SupposingMisthenumberoftrainingsamples,

xjisthefaceimagevectorwhichisderivedfromthejthpicture,

sothecovariancematrixofthewholesamplesis:

M

s=å

（x-u）（x-u）T （5）

T j jj=1

Andtheμistheaverageimagevectorofthetrainingsamples:

å

j

u=1Mx

Mj=1

（6）

1 2

OrderingA=[x

-u,x

-u,L,x

-u],soS

=AATanditsdemisionisD´

D.（7）

T

AccordingtotheprincipleofK-Ltransform,thecoordinateweachieved is com-posed of eigenvector corresponding to nonzero

eigenvalueofmatrixComputingouttheeigenvalueandOrthogonal

normalizedvectorofmatrixD×

Ddirectlyisdiffi-cult.SoaccordingtotheSVDprinciple,itcanfigureouttheeigenvalueandeigen-vectorofmatrixthroughgettingtheeigenvalueandeigenvector

ofmatrix

li（i=1,2,L,r）is r nonzero eigenvalue of matrix

vi:

is the

eigenvector corre-sponding toli

so the orthogonal normalized

lieigenvectorofmatrix isasbel-low:

This is the eigenvector of Arranging its eigenvalues

accordingtothesize:

l1³

l2³

L³

li,itscorrespondingeigenvectoris

mi. In this way, each face image can project on the sub-space

composedof,

m1m2…mr.Inordertoreducethedimension,itcan

selecttheformerdeigenvectorsassub-space.Itcanselectdbiggesteigenvectorsaccordingtotheenergyproportionwh