Emotion Recognition A Pattern Analysis Approach

583 Pages·2015·11.52 MB·English

by  Amit Konar, Aruna Chakraborty

Emotion
Recognition
A Pattern Analysis Approach
Amit Konar & Aruna Chakraborty

EMOTION
RECOGNITION

EMOTION
RECOGNITION
A Pattern Analysis Approach
Editedby
AMITKONAR
ArtificialIntelligenceLaboratory
DepartmentofElectronicsandTelecommunicationEngineering
JadavpurUniversity
Kolkata,India
ARUNACHAKRABORTY
DepartmentofComputerScience&Engineering
St.Thomas’CollegeofEngineering&Technology
Kolkata,India
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LibraryofCongressCataloging-in-PublicationData:
Konar,Amit.
Emotionrecognition:apatternanalysisapproach/AmitKonar,ArunaChakraborty.
pagescm
Includesindex.
ISBN978-1-118-13066-7(hardback)
1.Human-computerinteraction. 2.Artificialintelligence. 3.Emotions–Computersimulation.
4.Patternrecognitionsystems. 5.Context-awarecomputing. I.Chakraborty,Aruna,1977- II.Title.
QA76.9.H85K6552014
004.01’9–dc23
2014024314
PrintedintheUnitedStatesofAmerica.
10 9 8 7 6 5 4 3 2 1
Toourparents

CONTENTS
Preface xix
Acknowledgments xxvii
Contributors xxix
1 IntroductiontoEmotionRecognition 1
AmitKonar,AnishaHalder,andArunaChakraborty
1.1 BasicsofPatternRecognition, 1
1.2 EmotionDetectionasaPatternRecognitionProblem, 2
1.3 FeatureExtraction, 3
1.3.1 FacialExpression–BasedFeatures, 3
1.3.2 VoiceFeatures, 7
1.3.3 EEGFeaturesUsedforEmotionRecognition, 9
1.3.4 Gesture-andPosture-BasedEmotionalFeatures, 11
1.3.5 MultimodalFeatures, 12
1.4 FeatureReductionTechniques, 15
1.4.1 PrincipalComponentAnalysis, 15
1.4.2 IndependentComponentAnalysis, 16
1.4.3 EvolutionaryApproachtoNonlinearFeatureReduction, 16
1.5 EmotionClassification, 17
1.5.1 NeuralClassifier, 17
1.5.2 FuzzyClassifiers, 21
1.5.3 HiddenMarkovModelBasedClassifiers, 22
1.5.4 k-NearestNeighborAlgorithm, 22
1.5.5 Na¨ıveBayesClassifier, 23
vii
viii CONTENTS
1.6 MultimodalEmotionRecognition, 24
1.7 StimulusGenerationforEmotionArousal, 24
1.8 ValidationTechniques, 26
1.8.1 PerformanceMetricsforEmotionClassification, 27
1.9 Summary, 27
References, 28
AuthorBiographies, 44
2 ExploitingDynamicDependenciesAmongActionUnitsfor
SpontaneousFacialActionRecognition 47
YanTongandQiangJi
2.1 Introduction, 48
2.2 RelatedWork, 49
2.3 ModelingtheSemanticandDynamicRelationshipsAmongAUs
WithaDBN, 50
2.3.1 ADBNforModelingDynamicDependencies
amongAUs, 51
2.3.2 ConstructingtheInitialDBN, 54
2.3.3 LearningDBNModel, 55
2.3.4 AURecognitionThroughDBNInference, 59
2.4 ExperimentalResults, 60
2.4.1 FacialActionUnitDatabases, 60
2.4.2 EvaluationonCohnandKanadeDatabase, 61
2.4.3 EvaluationonSpontaneousFacialExpression
Database, 62
2.5 Conclusion, 64
References, 64
AuthorBiographies, 66
3 FacialExpressions:ACross-CulturalStudy 69
ChandraniSaha,WashefAhmed,SomaMitra,DebasisMazumdar,
andSushmitaMitra
3.1 Introduction, 69
3.2 ExtractionofFacialRegionsandEkman’sActionUnits, 71
3.2.1 ComputationofOpticalFlowVectorRepresentingMuscle
Movement, 72
3.2.2 ComputationofRegionofInterest, 73
3.2.3 ComputationofFeatureVectorsWithinROI, 74
3.2.4 FacialDeformationandEkman’sActionUnits, 75
3.3 CulturalVariationinOccurrenceofDifferentAUs, 76
3.4 ClassificationPerformanceConsideringCulturalVariability, 79
3.5 Conclusion, 84
References, 84
AuthorBiographies, 86