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
识别
辨认
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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_FAST_RCNN_TRAINPTBASE_LR（更快）0001，LR_POLICY（更快）“STEP”，STEPSIZE（更快）30000，显示（更快）20，AVERAGE_LOSS（更快）100，动量（更快）09，WEIGHT_DECAY更快）00005。STAGE2_RPN_TRAINPTBASE_LR（更快）0001，LR_POLICY（更快）“STEP”，STEPSIZE（更快）60000，显示（更快）20，AVERAGE_LOSS（更快）100，动量（更快）09，WEIGHT_DECAY更快）00005。网络的部分参数设置如下DATA_PARAM_STR_NUM_CLASSES9，CLS_SCORE_NUM_OUTPUT9，BBOX_PRED_NUM_OUTPUT36。35培训和测试我们将所有区域提案的08IOU重叠视为正片，其余视为片盒类的负片。我们偏向正窗口采样，因为它们与背景相比极其罕见。4实验结果为了提高实验结果的可靠性，使用三种不同深度的网络来训练和测试数据，即VGG_CNN_M_10248，
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