鲁棒的分区域边界球描述子用于三维人脸识别

鲁棒的分区域边界球描述子用于三维人脸识别/PAPEREDU1中国科技论文在线ROBUSTREGIONALBOUNDINGSPHEREREPRESENTATIONFOR3DFACERECOGNITIONMINGYUE,RUANQIUQIINSTITUTEOFINFORMATIONSCIENCE,BEIJINGJIAOTONGUNIVERSITY,BEIJING1000445BRIEFAUTHORINTRODUCTIONMINGYUE,1984，YUEMING明悦ISCURRENTLYPURSUINGTHEPHDDEGREEATTHEINSTITUTEOFINFORMATIONSCIENCE,BEIJINGJIAOTONGUNIVERSITYHERRESEARCHINTERESTSINCLUDEIMAGEPROCESSING,PATTERNRECOGNITION,3DFACERECOGNITIONANDRECONSTRUCTION,ETCABSTRACTAROBUSTREGIONALBOUNDINGSPHEREREPRESENTATIONRRBSRISINTRODUCEDTOFACILITATE3DFACERECOGNITIONINOURFRAMEWORK,WEFIRSTSEGMENTAGROUPOFREGIONSONEACH3DFACIALPOINTCLOUDBYCURVATUREINFORMATIONANDFACIALSHAPECHARACTERISTICSTHEN,THEEXTRACTEDREGIONSAREPROJECTEDONTHEBOUNDINGSPHERICALBANDSINORDERTOREFLECTTHEDISTINCTIVESHAPEINFORMATIONOFFACIALDIFFERENTREGIONSNEXT,ANORTHOGONALREGIONALANDGLOBALREGRESSIONORGRISUTILIZEDTOEXTRACTTHE10DISCRIMINANTFEATUREVECTORSEXPERIMENTALRESULTSBASEDONTHEDIFFERENT3DFACEDATABASESDEMONSTRATETHATBALANCINGREGIONALANDGLOBALFACIALCHARACTERISTICSALLOWFORTHEHIGHQUALIFIEDPERFORMANCECOMPAREDWITHTHEPREVIOUSPOPULARAPPROACHES,OURFRAMEWORKHASACONSISTENTLYBETTERPERFORMANCEINTERMSOFEFFECTIVENESS,ROBUSTNESS,ANDUNIVERSALITYKEYWORDSREGIONALBOUNDINGSPHEREREPRESENTATION3DFACERECOGNITIONORTHOGONALREGIONALAND15GLOBALREGRESSION0INTRODUCTIONBIOMETRICSSYSTEMSHAVEBEENPRESENTEDFORSEVERALDECADESWITHWIDEAPPLICATIONS,SUCHASINTELLIGENTMONITORING,COMPUTERAIDEDSECURITYSYSTEM,ANDMEDICALRESCUEAMONGTHEM,FACE20RECOGNITIONHASAHIGHLEVELPREFERENCEFORAHUGENUMBEROFRESEARCHERSANDORGANIZATIONS,MAINLYBECAUSEOFITSNONINVASIVENESSANDUSERFRIENDLINESSHOWEVER,FROMTHEPERSPECTIVEOFPRACTICALAPPLICATIONS,FACERECOGNITIONDEVELOPEDBY2DIMAGESHASBEENHINDEREDBYTHEOBSTACLESINDUCEDBYPOSE,LIGHTING,EXPRESSIONS,ANDOTHERVARIEDCHARACTERISTICSINUNCONTROLLEDENVIRONMENTSWITHTHELOWEREDCOSTOF3DDIGITALCAPTURINGSYSTEMS,FACIALRECOGNITIONIN3DDATA25HASBEENINTRODUCEDTOSOLVETHECHALLENGINGISSUESUSINGAVARIETYOFMETHODSASUFFICIENTBROADINVESTIGATIONOFFACERECOGNITIONHASBEENPROVIDEDIN1,SPECIFICALLYON3DFACERECOGNITIONEMPIRICALSTUDYSHOWSTHATFACIALSHAPEHASSIGNIFICANTVARIATIONSINTERMSOFDIFFERENTREGIONSONTHEFACIALSURFACEINORDERTOBETTERREFLECTFACIALANATOMICALSTRUCTUREANDDESCRIBETHEDISCRIMINANTFEATURES,WEINTRODUCESEGMENTINGSCHEMETOADDRESSTHEMAJOR30CHALLENGESEFFECTINGTHEPERFORMANCEIMPROVEMENTANDPRESENTANEWRECOGNITIONFRAMEWORKTO3DFACIALDATAOURRESEARCHCONSISTSOFTHREEIMPORTANTPROCEDURESFACIALREGIONSEGMENTATION,FEATUREREPRESENTATION,ANDFEATUREEXTRACTIONINOURFRAMEWORK,AGROUPOFFACIALLOCALREGIONSCANBECOARSELYLOCATEDBYCURVATUREINFORMATIONWHICHPOSSESSESMOREDISCRIMINANTPOWERTHEN,WEEXPLOITTHEGLOBALBOUNDING35SPHEREDESCRIPTORTOTHESUBREGIONSONTHEFACEFORDECREASINGTHEINFLUENCEOFEXPRESSIONSANDPOSESANDTHEREGIONSWITHINTHESHAPEBANDSCANBECONVERTEDINTOTHESPHERICALDOMAINSBASEDONTHEREGIONALBOUNDINGSPHERESTOOBTAINOURREGIONALBOUNDINGSPHEREREPRESENTATIONRBSRREGIONALANDGLOBALREGRESSIONWITHORTHOGONALCONSTRAINTSISINTRODUCEDTOGRASPTHEMANIFOLDSTRUCTUREOFOURFEATUREREPRESENTATIONTHELOWERDIMENSIONALDISCRIMINANTFEATUREVECTORSCANBE40EXTRACTEDTODESCRIBETHEREGIONALCHARACTERISTICSFORACHIEVINGTHE3DFACERECOGNITIONOURMETHODISMOREROBUSTTOIMAGEARTIFACTS,LIGHTINGVARIANCE,WINKLES,ANDOCCLUSIONSAND/PAPEREDU2中国科技论文在线CORRUPTIONSANDSHOWSTHEGENERALIZATIONBASEDONTHEDIFFERENTCHALLENGINGDATABASES1FACIALREGIONALSEGMENTATIONANDFEATUREREPRESENTATIONTHEORIGINAL3DFACIALIMAGESINTHEDATABASESUSUALLYCONTAINSOMENONFACIALAREAS,SUCHAS45EARS,NECKSANDSHOULDERSASSHOWNINFIGURE1BYCOMBININGTHE2DTEXTUREIMAGEANDITSCORRESPONDINGVALIDPOINTMATRIX,THEMAINFACIALAREACANBECOARSELYEXTRACTEDAXISANGLEREPRESENTATIONISUSEDTOALIGNTHEINPUTWITHTHEREFERENCEMODELFIXEDANDCORRECTLARGEPOSEVARIATIONSMOREDETAILSCANBEFOUNDIN3THEN,WITHTHEDIFFERENTVALUESANDDIRECTIONSOFCURVATURE4,THEDIFFERENTAREASOFAFACECANBECOARSELYDETECTED50FIGURE13DFACIALREGIONALSEGMENTATIONINORDERTOBETTERREFLECTTHEFACIALSURFACESHAPEANDINCREASETHEDISCRIMINATION,ANOVELDESCRIPTORISPROPOSEDFORAGROUPOFFACIALREGIONS,DENOTEDASBOUNDINGSPHEREREPRESENTATION55BSRFOREACHREGIONONA3DFACIALIMAGE,THEDESCRIPTORISIMAGEDASTHEPROJECTIONOFTHERELATIVEPOSITIONOFAFACIALPOINTCLOUDINTOBOUNDINGSPHERESCENTEREDASTHECENTROIDPOINTSOFTHEREGIONSTHERATIOBETWEENTHEDISTANCESOFPOINTSOFTHEREGIONANDITSCORRESPONDINGCENTROIDANDRADIUSOFBOUNDINGSPHEREREFERSTOTHEVALUEOFSPHERICALPOINTSTHEVALUESOFPOINTSONTHEBOUNDINGSPHERECANBEDEFINEDASFOLLOWS,60222/IIXXIIYYIIZZIBSJBSRCPCPCPCR1WHEREIPISTHECOORDINATEVALUEOFEACHALIGNED3DFACIALREGIONPOINT,BSJRDENOTESTHEMAXIMUMDIFFERENCEAMONGTHETHREEDIFFERENTDIRECTIONS,XYZONTHECARTESIANCOORDINATESASTHERADIUSOFTHEREGIONALBOUNDINGSPHERE2ROBUSTORTHOGONALREGIONALANDGLOBALREGRESSIONSFOR65DISCRIMINANTFEATUREEXTRACTIONEFFICIENTALGORITHMSFORHIGHDIMENSIONALDATAREQUIREADIMENSIONREDUCTIONPROCEDURE,ANDCOMPACTDISCRIMINANTFEATURESNEEDTOBEEXTRACTEDTODESCRIBETHEORIGINALFACIALDATAHOWEVER,THEREARESOMEREMAININGARTIFACTSLEFTFROMTHEREGIONSEGMENTATION,SUCHASSOMESTRETCHEDORMISALIGNEDIMAGES,EXPRESSIONVARIANTS,HAIROCCLUSIONS,ANDLARGEDATANOISESANDCORRUPTIONS70HERE,WEINTRODUCETHEORTHOGONALREGIONALANDGLOBALREGRESSIONFOREFFECTIVELYDISCRIMINANTFEATUREEXTRACTIONINMULTIREGIONSLEARNING,WEAREGIVENASETOFFACIALIMAGESWITHKREGIONS1,1,JMJJIIIBSRYJK,WHEREJMJIBSRDENOTESTHEBSRDESCRIPTOROF/PAPEREDU3中国科技论文在线THEITHFACIALSAMPLEFORTHEJTHREGION,JIYDENOTESTHECORRESPONDINGOUTPUT,JMISTHE75NUMBEROFPOINTSFORJTHREGION,AND1KJJMMISTHETOTALDIMENSIONOFTHEORIGINAL3DFACIALBSRDESCRIPTORLET1,JJMNUMJJJTMBSRBSRBSRDENOTETHEDATAMATRIXOFBSRDESCRIPTORSFORTHEJTHREGIONAMONGNUM3DFACIALIMAGESINOURAPPLICATIONS,THECLEANANDWELLALIGNEDDESCRIPTORSCANBETREATEDASLINEARLYCORRELATEDWITHCLASSICALREGRESSIONMODEL,1,JJTJYRBSRJK,WHEREJMDJRISTHEREGRESSIONMATRIXFORTHE80JTHREGIONTHEREGRESSIONVECTORSFORALLKREGIONSFROMTHEREGRESSIONMATRIX1,MDKRRRWHICHNEEDTOBEESTIMATEDFROMTHEDATAHOWEVER,INMANYREALWORLDSITUATIONS,THEOBSERVATIONS,INOURCASETHEEXPLANATORYVARIABLESJBSRAREPERTURBEDBYSMALL,BUTDENSELYSUPPORTED,NOISESTHECLASSICALREGRESSIONFRAMEWORKSHAVEBEENEXTENDEDTOTHEMODELBYADDINGANOISETERME,WHICHCANMINIMIZETHEINFLUENCEOFTHEDATA85NOISESTHEN,WECANEFFECTIVELYDECREASETHEINFLUENCEOFTHEDATANOISESANDPRESERVETHELINEARSTRUCTUREBETWEENTHEEXPLANATORYANDRESPONSEVARIABLESTHERELATIONSHIPOFTHEEXPLANATORYANDRESPONSEVARIABLESCANBEDENOTEDASFOLLOWS,1,JJTJJYRBSREJK2INORDERTOBETTERCOPEWITHTHEDATAANDREFLECTTHEFACIALGLOBALPROPERTIES,WEINTRODUCEA90NONPARAMETRICAPPROACH7FOROUTOFSAMPLEEXTRAPOLATIONTOOBTAINTHEGLOBALREGRESSIONMATRIXBASEDONTHEREGIONALBSRDESCRIPTORSWEMAPTHEREGIONALFACIALBSRDESCRIPTORINTOAHILBERTSPACEANDD,IEJJTJJYRBSRE,WHEREJRISTHEREGIONALREGRESSIONMATRIXFROMTODANDJDEISNOISETERMASARESULT,THEOBJECTIVEFUNCTIONFORORTHOGONALREGIONALANDLOCALREGRESSIONCANBEREWRITTENAS9522,11221MIN,STJJMKTJJIIIFFRREEYIJMTTIIFFIRBSREYRRBSREYRYYI3ACCORDINGTOTHELITERATURE7,THEDISCRIMINANTFEATUREVECTORYCANBECOMPUTEDBY111111TTTXMMYYHKHIHKYYHKHIHKMM4WHERE111TMMHIMDENOTEASTHEGLOBALCENTERINGMATRIX,MXKDENOTEASAVECTORWITHITSITHELEMENT22EXP/TXIIIKBSRBSRBSRBSR,ANDIXISTHE100ITHINDIVIDUALINTHEFACIALIMAGESETINORDERTOAVOIDOVERFITTING,WEPERFORMLOCALPCATOREDUCETHEDIMENSIONOFEACHFACIALREGIONBSRDESCRIPTORASPREPROCESSINGOURDISCRIMINANTFEATUREVECTORSYCANEFFICIENTLYIMPROVETHEDISCRIMINANTFEATUREEXTRACTION,AVOIDINGTHECORRUPTEDDATAINVOLVEDANDINFLUENCEOFMISALIGNMENTITEFFICIENTLYPRESERVESTHERELATIONSHIPOFDIFFERENTFACIALREGIONSINTHEHOMOGENEOUSSAMPLESANDENHANCESTHEDISCRIMINANTBETWEENTHE105HETEROGENEOUSSAMPLES/PAPEREDU4中国科技论文在线3EXPERIMENTSINTHEEXPERIMENTALSECTION,WETESTTHEPERFORMANCEOFOUR3DFACERECOGNITIONFRAMEWORKINBOTHIDENTIFICATIONANDVERIFICATIONSCENARIOSFROMTHEPREPROCESSEDIMAGES,WECANEXTRACTTHE110DIFFERENTDISCRIMINATIVEFEATURESTOREPRESENTTHEINDIVIDUALSANDCOMPARETHEACCURACYWITHOTHERPOPULARMETHODSBASEDONTHESAMEAPPLICATIONPURPOSETHESIMILARITYMEASUREISEUCLIDEANDISTANCE31EXPERIMENTSWITHTHEDIFFERENTDESCRIPTORSHERE,WEEVALUATETHECHALLENGINGISSUESOF3DFACERECOGNITIONDISCUSSEDONTHEFRGCV2115DATABASE8,WHICHISTHELARGESTPUBLICLYAVAILABLEDATABASE,INCLUDING4,007SCANSOF466SUBJECTSINTOTALINTHESEEXPERIMENTS,WEUSEOURPROPOSEDRRBSRFEATUREDESCRIPTORTOCHARACTERIZETHESUBJECTSANDCOMPARETHEIDENTIFICATIONPERFORMANCEWITHTHERAWDEPTHIMAGES,SPINIMAGES9ANDSFRDESCRIPTORS10,RESPECTIVELYWEDEFINEFOURDIFFERENTTYPECONFIGURATIONSFORTHEPERFORMANCEEVALUATIONASSHOWNINTABLE1FOREACHCONFIGURATION,WE120PRESERVEONLYTHEINDIVIDUALSFROMTHEDATABASETHOSEHAVEATLEAST1ISAMPLES,ANDWERANDOMLYCHOSENITRAININGSAMPLESPERCLASS,WHILEASSIGNINGTHERESTTOTHETESTSET2TABLE1TESTCONFIGURATIONSWITHTHEDIFFERENTTESTSETSCONFIGURATIONSI1234NUMBEROFSUBJECTS410384316285TRAININGSET4107689481140TESTSET3541318330032755125WESHOWTHERANK1IDENTIFICATIONACCURACYBASEDONTHEDIFFERENTDESCRIPTORSFROMTABLE2,OURRRBSRFEATUREDESCRIPTORSSIGNIFICANTLYOUTPERFORMTHEOTHERDESCRIPTORSONALLOFTESTCONFIGURATIONSTHEDEPTHIMAGEHADSLIGHTLYHIGHERACCURACYTHANTHESINGLESPINIMAGEANDTHESFRDESCRIPTORSALSOHAVEHIGHERRECOGNITIONPERFORMANCETHANTHERAWDEPTHIMAGESTHERESULTSDEMONSTRATETHEEFFECTIVENESSOFOURRRBSRDESCRIPTORONTHECHALLENGINGFACIALVARIATIONSAND130THESENSORINFORMATIONHOWEVER,FORLARGEILLUMINATIONVARIATIONS,SPINIMAGESANDSFRDESCRIPTORSMAYBECOMPLETELYCORRUPTEDANDINEVITABLETOINFLUENCETHEACCURACYEVENTHOUGHTHEREARESOMEROBUSTESTIMATIONALGORITHMS5TORECOVERTHECORRUPTEDIMAGES,THEYREQUIRESOMEPERFECTIMAGESASTHETRAININGSET,WHICHISDIFFICULTTOOBTAININTHEWIDEAPPLICATIONSESPECIALLYFORLARGEEXPRESSIONVARIATIONS,FOREXAMPLETHEMAGNITUDEOFOPENINGMOUTHANDTHELOWERCHIN135CHANGESOURRRBSRDESCRIPTORCANEFFECTIVELYCONVERTTHEORIGINALPOSITIONSONTHETHREEAXESINTOAREPRESENTATIONVECTORWHICHCANBETREATEDASTHECOMPLEMENTARYOFTHEDEPTHANDINTENSITYINFORMATIONWITHMOREDISCRIMINATIVEPROPERTIESANDROBUSTTOEXPRESSIONVARIATIONSTHISISILLUSTRATEDTHATTHEDISTINCTIMPROVEMENTONOURDESCRIPTORCOMPAREDWITHTHEOTHERCOMMONLYUSEDREPRESENTATIONS140TABLE2RANK1IDENTIFICATIONRESULTSWITHTHEDIFFERENTDESCRIPTORSCONFIGURATIONSI1234RRBSR5417612568037498DEPTHIMAGES445586458976314SPINIMAGES7427521256716319SFRDESCRIPTORS5227598566577289WEALSOSHOWTHECUMULATIVEMATCHCHARACTERISTICSCMCCURVESOFTHEMETHODSFORFRGCV2DATABASEASILLUSTRATEDINFIGURE2OURRRBSRDESCRIPTORSCANPROVIDESIGNIFICANTLY145HIGHQUALITYRESULTSTHROUGHTHEDIFFERENTRANKSTHISISMAINLYBECAUSEOURFEATURESTHATREFLECTTHE/PAPEREDU5中国科技论文在线CHANGESOFFACIALSHAPESAREINSENSITIVETOTHEVARIATIONSOFPOSESORILLUMINATIONSCONSIDERINGTHEINTRINSICCHARACTERISTICOFTHEDIFFERENTFACIALREGIONS,RRBSRAREMOREROBUSTTOFACIALVARIATIONSCOMPAREDWITHTHEOTHERDESCRIPTORSFACIALEXPRESSIONSMAINLYREFLECTTHESHAPEVARIATIONSONTHEMOUTHANDCHINMATHEMATICALLY,THEVARIATIONSCANBETREATEDASNONRIGIDTRANSFORMATIONSAND150DETERIORATETHELINEARSTRUCTUREOFTHEFACIALSURFACETHUS,OURORGRFEATUREEXTRACTIONMETHOD,SINCETHEYHAVEBALANCEDTHEINFLUENCEOFTHEFACIALRIGIDANDNONRIGIDAREASONTHEWHOLE3DFACE,CANEFFECTIVELYPRESERVETHELINEARCHARACTERISTICSANDCONQUERTHEIMPACTOFEXPRESSIONVARIATIONSFIGURE2THECMCCURVESOFFRGCV203DFACEDATABASE1554CONCLUSIONINTHISPAPER,WEPROPOSEANEW3DFACERECOGNITIONFRAMEWORKRRBSRBYCOMBININGANOVELBSRDESCRIPTORANDANOVELDISCRIMINANTFEATUREEXTRACTIONBASEDONORGRWEHAVEUTILIZEDASHAPEBANDSMETHODFORREFINEDFACIALREGIONSEGMENTATIONINTHEORIGINAL3DPOINT160CLOUDSBSRDESCRIPTORCANEFFICIENTLYREFLECTTHEREGIONALSURFACESHAPEANDENHANCEIMAGELOWLEVELFEATURES,WHICHAREEQUALTOENHANCEKEYFACIALELEMENTINFORMATIONSUCHASTHENOSE,EYES,ANDMOUTHOURORGRMODELBASEDONREGIONALANDLOCALREGRESSIONSCHEMEEFFECTIVELYBALANCESTHEREGIONALNEIGHBORSTRUCTUREOFAFACIALMANIFOLDANDTHEGLOBALCHARACTERISTICSOFFACIALSHAPEASARESULT,OURMETHODNICELYINHERITSTHEABILITYOFLOCALPRESERVATIONANDINCREASE165SEPARABILITY,WHICHOVERCOMESEXPRESSIONANDPOSESVARIATIONSTOSOMEEXTENTFINALLY,EXPERIMENTALRESULTSSHOWTHATTHEPERFORMANCEOFOURPROPOSEDRRBSRFRAMEWORKISBETTERTHANOTHERPOPULARAPPROACHESWITHAGOODGENERALIZATIONACKNOWLEDGEMENTSTHANKSTOHELPFULPEOPLEANDCOMPANIESTHISWORKISSUPPORTEDBYNATIONALNATURALSCIENCE170FOUNDATIONNO60973060SPECIALIZEDRESEARCHFUNDFORTHEDOCTORALPROGRAMOFHIGHEREDUCATIONNO200800040008REFERENCES1KWBOWYER,KCHANG,ANDPFLYNNASURVEYOFAPPROACHESANDCHALLENGESIN3DANDMULTIMODAL1753D2DFACERECOGNITIONCOMPUTERVISIONANDIMAGEUNDERSTANDING,1011115,20062RSLLONCH,EKOKIOPOULOU,ITOSIC,ANDPFROSSARD3DFACERECOGNITIONWITHSPARSESPHERICALREPRESENTATIONSPATTERNRECOGNITION,433824834，20103YUEMING，QIUQIRUANROBUSTSPARSEBOUNDINGSPHEREFOR3DFACERECOGNITIONSUBMITTEDTOIMAGEANDVISIONCOMPUTING1804ABMORE