Facial Expression Recognition with Faster R-CNN.docx
用更快的R-CNN进行人脸表情识别【中文3606字】
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用更快
cnn
进行
表情
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中文
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用更快的R-CNN进行人脸表情识别【中文3606字】,用更快,cnn,进行,表情,识别,辨认,中文
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AVAILABLEONLINEATWWWSCIENCEDIRECTCOMSCIENCEDIRECTPROCEDIACOMPUTERSCIENCE1072017135140INTERNATIONALCONGRESSOFINFORMATIONANDCOMMUNICATIONTECHNOLOGYICICT2017FACIALEXPRESSIONRECOGNITIONWITHFASTERRCNNJIAXINGLIA,DEXIANGZHANGA,JINGJINGZHANGA,JUNZHANGA,TENGLIA,YIXIAA,QINGYANA,ANDLINAXUNAATHESCHOOLOFELECTRICALENGINEERINGANDAUTOMATIONANHUIUNIVERSITY,HEFEI230601,CHINACORRESPONDINGAUTHORZDXDZXY126COMTEL086055163861094ABSTRACTINORDERTOAVOIDTHECOMPLEXEXPLICITFEATUREEXTRACTIONPROCESSANDTHEPROBLEMOFLOWLEVELDATAOPERATIONINVOLVEDINTRADITIONALFACIALEXPRESSIONRECOGNITION,WEPROPOSEDAMETHODOFFASTERRCNNFASTERREGIONSWITHCONVOLUTIONALNEURALNETWORKFEATURESFORFACIALEXPRESSIONRECOGNITIONINTHISPAPERFIRSTLY,THEFACIALEXPRESSIONIMAGEISNORMALIZEDANDTHEIMPLICITFEATURESAREEXTRACTEDBYUSINGTHETRAINABLECONVOLUTIONKERNELTHEN,THEMAXIMUMPOOLINGISUSEDTOREDUCETHEDIMENSIONSOFTHEEXTRACTEDIMPLICITFEATURESAFTERTHAT,RPNSREGIONPROPOSALNETWORKSISUSEDTOGENERATEHIGHQUALITYREGIONPROPOSALS,WHICHAREUSEDBYFASTERRCNNFORDETECTIONFINALLY,THESOFTMAXCLASSIFIERANDREGRESSIONLAYERISUSEDTOCLASSIFYTHEFACIALEXPRESSIONSANDPREDICTBOUNDARYBOXOFTHETESTSAMPLE,RESPECTIVELYTHEDATASETISPROVIDEDBYCHINESELINGUISTICDATACONSORTIUMCLDC,WHICHISCOMPOSEDOFMULTIMODALEMOTIONALAUDIOANDVIDEODATAEXPERIMENTALRESULTSSHOWTHEPERFORMANCEANDTHEGENERALIZATIONABILITYOFTHEFASTERRCNNFORFACIALEXPRESSIONRECOGNITIONTHEVALUEOFTHEMAPISAROUND082KEYWORDSFACIALEXPRESSIONRECOGNITIONFASTERRCNNDEEPLEARNINGGRAPHICSPROCESSINGUNIT1INTRODUCTIONFACIALEXPRESSIONISAKINDOFEFFECTIVEWAYOFHUMANCOMMUNICATIONFACIALEXPRESSIONRECOGNITIONISTHEKEYTECHNOLOGYFORREALIZINGHUMANCOMPUTERINTERACTIONTOBETHEEMOTIONALCOMPUTINGSYSTEMFACIALEXPRESSIONHASABROADAPPLICATIONPROSPECTINMANYRESEARCHFIELDS,SUCHASVIRTUALREALITY,VIDEOCONFERENCE,CUSTOMERSATISFACTIONSURVEYANDOTHERFIELDSDESPITEOFGREATENCOURAGINGPROGRESSHAVEBEENMADEINTHISRESEARCHFIELD,THEREARESTILLMANYPROBLEMSEXISTINGONONEHAND,THETRADITIONALFEATUREEXTRACTIONMETHODSARECOMPLETELYRELIEDONHUMANEXPERIENCE,WHICHISSTILLTOOCOMPLICATEDFORREALAPPLICATIONTHEREFORE,THETRADITIONALMETHODSAREVERYDIFFICULTTOEXTRACTUSEFULFEATURESCOMPREHENSIVELYANDEFFECTIVELYONTHEOTHERHAND,THETRADITIONALMETHODSCANNOTDISPOSETHEBIGDATAANDACHIEVEBETTERPERFORMANCESOITISNOTEASYTOMEETTHEREALAPPLICATIONREQUIREMENTINMOSTSITUATIONS,THISKINDOFMETHODCANNOTBEEMPLOYEDEFFECTIVELY187705092017THEAUTHORSPUBLISHEDBYELSEVIERBVTHISISANOPENACCESSARTICLEUNDERTHECCBYNCNDLICENSEHTTP/CREATIVECOMMONSORG/LICENSES/BYNCND/40/PEERREVIEWUNDERRESPONSIBILITYOFTHESCIENTIFICCOMMITTEEOFTHE7THINTERNATIONALCONGRESSOFINFORMATIONANDCOMMUNICATIONTECHNOLOGYDOI101016/JPROCS201703069136JIAXINGLIETAL/PROCEDIACOMPUTERSCIENCE1072017135140TOADDRESSTHEABOVEISSUES,WEPROPOSEANENDTOENDRECOGNITIONMETHODBASEDONFASTERRCNN1THEPROPOSEDMETHODCANBEUSEDTOSOLVETHEEXISTINGPROBLEMSFIRST,THEREGIONPROPOSALNETWORKSRPNS1AREUSEDTOPREDICTEFFICIENTANDACCURATEREGIONPROPOSAL4THEPIPELINEOFTHEPROPOSEDMETHODJUSTUSEONECONVOLUTIONALNEURALNETWORKCNNFORALLPURPOSESO,THEREGIONPROPOSALISNEARLYCOSTFREEBYSHARINGCONVOLUTIONALFEATURESOFTHEDOWNFLOWDETECTIONNETWORKSECOND,THERPNALSOIMPROVEDTHEREGIONALPROPOSAL4QUALITYANDTHEACCURACYOFTHEOVERALLTARGETDETECTION2FASTERRCNNALGORITHMFASTERRCNN1CANBESIMPLYREGARDEDASTHESYSTEMCONSISTINGOFREGIONALPROPOSALNETWORKANDFASTREGIONSWITHCONVOLUTIONALNEURALNETWORKFEATURESFASTRCNNTHEREGIONALPROPOSALNETWORKISUSEDTOINSTEADSELECTIVESEARCHALGORITHM4OFFASTRCNNTHEPROPOSEDMETHODFOCUSESONSOLVINGTHREEPROBLEMS1HOWTODESIGNAREGIONALPROPOSALNETWORK2HOWTOMAKEPROPOSALNETWORKREGION3HOWTOSHAREFEATUREEXTRACTIONNETWORK21CANDIDATEREGIONANCHORSCHARACTERISTICSCANBESEENASA256CHANNELIMAGEWITHASCALEOF5139,FOREACHPOSITIONOFTHEIMAGETHEMETHODCONSIDERSTHENINEPOSSIBLECANDIDATEWINDOWS,WHICHARETHREEAREASOF128,256,512MULTIPLIEDBYTHREERATIOSOF11,12,21THESECANDIDATEWINDOWSARESAIDTOBEAS“ANCHORS“FIG1SHOWSTHEANCHOR5139CENTER,ASWELLAS9ANCHOREXAMPLESFIG1THE5139ANCHORCENTERSASWELLAS9ANCHOREXAMPLESEACHPOSITIONOFTHEOUTPUTOFCLASSIFICATIONLAYER6CLS_SCORESHOWSTHEPROBABILITYTHATTHE9ANCHORSBELONGTOTHEFOREGROUNDANDBACKGROUNDANDEACHPOSITIONOFTHEOUTPUTOFREGRESSIONLAYER6BBOX_PREDSHOWSTHATTHECORRESPONDINGWINDOWOFTHE9ANCHORSSHOULDBETRANSLATEDTOSCALEPARAMETERSFOREACHLOCATION,THECLASSIFICATIONLAYEROUTPUTSTHEPROBABILITYOFTHEFOREGROUNDANDTHEBACKGROUNDFROMTHE256DIMENSION,WHILETHEREGRESSIONLAYEROUTPUTS4TRANSLATIONSCALINGPARAMETERSFROMALOCALRESPECTIVE,THETWOLAYERSARETHEWHOLECONNECTIONNETWORK,WHILEFROMAGLOBALRESPECTIVE,SINCETHENETWORKSHARESAMEPARAMETERSINALLPOSITIONS5139,THENETWORKUSEDINTHEPRESENTSTUDYISACTUALLYWITHASIZEOF1122SHARINGFEATUREREGIONPROPOSALNETWORKRPNANDFASTRCNNREQUIREANORIGINALFEATUREEXTRACTIONNETWORKTHEIMAGENETCLASSIFICATIONLIBRARYISUSEDTOTRAINTHEINITIALPARAMETER0OFTHENETWORKTHENTHENETWORKISFINETUNEDBYTHESPECIFIEDDATASETTHEPROPOSEDMETHODPROVIDESTHREEMETHODS1TRAINRPNTOEXTRACTTHEANCHORSONTHETRAININGSETFROM02TRAINFASTRCNNBYUSINGTHEANCHORSFROM0,ANDTHEPARAMETERISDENOTEDAS13TRAINRPNFROM1FIG2SHOWSTHEDETAILSTEPSOFTHESHAREDFEATUREJIAXINGLIETAL/PROCEDIACOMPUTERSCIENCE1072017135140137INPUTTRAININGIMAGECONVFEATUREMAPROIPIFIG2THESTEPSOFTHESH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