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基于BN参数学习的人脸疼痛表情识别研究 Title:FacialPainExpressionRecognitionBasedonBNParameterLearning:AResearchStudy Abstract: Facialpainexpressionrecognitionhasgainedsignificantattentioninrecentyearsduetoitspotentialapplicationsinvariousdomains,includinghealthcare,human-computerinteraction,andpsychology.ThispaperpresentsacomprehensivestudyontherecognitionoffacialpainexpressionsusingaBayesianNetwork(BN)approachwithparameterlearning.TheresearchaimstoinvestigatetheeffectivenessofBN-basedmodelsinfacialpainexpressionrecognitionandexploretheirpotentialforreal-worldapplications. 1.Introduction 1.1Background 1.2Motivation 1.3Objectives 1.4Methodology 2.LiteratureReview 2.1FacialExpressionRecognition 2.2PainExpressionRecognition 2.3BayesianNetworksinFacialExpressionRecognition 2.4SummaryofExistingResearch 3.Methodology 3.1DatasetCollection 3.2PreprocessingTechniques 3.3FeatureExtraction 3.4BayesianNetworkModel 3.5ParameterLearningTechniques 3.6EvaluationMetrics 4.ExperimentalResults 4.1DatasetDescription 4.2ExperimentalSetup 4.3PerformanceEvaluation 4.4ComparisonwithExistingMethods 4.5DiscussionofResults 5.ApplicationScenarios 5.1Healthcare 5.2Human-ComputerInteraction 5.3Psychology 5.4FutureDirectionsandPotentialChallenges 6.Conclusion 6.1SummaryofFindings 6.2Contributions 6.3LimitationsandFutureWork 1.Introduction 1.1Background: Facialexpressionrecognitionplaysavitalroleinunderstandinghumanemotionsandtheirunderlyingpsychologicalstates.Amongdifferentemotionalexpressions,painexpressionrecognitionisofparticularimportanceinhealthcaresettings,whereaccuratepainassessmentiscrucialforpatientcare.Traditionalpainassessmentmethodsoftenrelyonself-reporting,whichcanbesubjectiveandunreliable.Automaticfacialpainexpressionrecognitionsystemsofferanobjectiveandnon-invasivealternative,providingvitalinformationforhealthcareprofessionals. 1.2Motivation: Despitetheadvancesinfacialexpressionrecognition,painexpressionrecognitionremainschallengingduetoitscomplexandvariednature.Existingapproachesoftenoverlooktheint