Emotion recognition: a pattern analysis approach
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Hoboken, New Jersey
Wiley
[2015]
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| Online Access: | Inhaltsverzeichnis |
| Physical Description: | xxxi, 548 Seiten Illustrationen, Diagramme |
| ISBN: | 9781118130667 9781118910603 |
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| 245 | 1 | 0 | |a Emotion recognition |b a pattern analysis approach |c edited by Amit Konar (Artificial Intelligence Laboratory, Department of Electronics and Telecommuniation Engineering, Jadavpur University, Kolkata, India), Aruna Chakraborty (Department of Computer Science & Engineering, St. Thomas' College of Engineering & Technology, Kolkata, India) |
| 264 | 1 | |a Hoboken, New Jersey |b Wiley |c [2015] | |
| 264 | 4 | |c © 2015 | |
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| 650 | 4 | |a Human-computer interaction | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Emotions / Computer simulation | |
| 650 | 4 | |a Pattern recognition systems | |
| 650 | 4 | |a Context-aware computing | |
| 650 | 7 | |a TECHNOLOGY & ENGINEERING / Electronics / Digital |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Computer Vision & Pattern Recognition |2 bisacsh | |
| 650 | 7 | |a Artificial intelligence |2 fast | |
| 650 | 7 | |a Context-aware computing |2 fast | |
| 650 | 7 | |a Emotions / Computer simulation |2 fast | |
| 650 | 7 | |a Human-computer interaction |2 fast | |
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CONTENTS
Preface x
Acknowledgments xxxii
Contributors xxix
I Introduction to Emotion Recognition 1
A mit Kona K Anishu Halden and A run a Chakmhorty I. I Basics of Pattern Recognition, î
1.2 Emotion Detection as a Pattern Recognition Problem, 2
1.3 Feature Extraction, 3
1.3.1 Facial Expression-Rased Features, 3
1.3.2 Voice Features, 7
1.3.3 EEC Features Used for Emotion Recognition, 9 t.3.4 Gesture- and Posture-Based Emotional Features, I I
1.3.5 Multimodal Features, 12
1.4 Feature Reduction Techniques, 15
1.4.1 Principal Component Analysis, 15
1.4.2 independent Component Analysis, 16
1.4.3 Evolutionary Approach to Nonlinear Feature Reduction, 16
1.5 Emotion Classification, 17
1.5.1 Neural Classifier, 17
1.5.2 Fuzzy Classifiers, 21
1.5.3 Hidden Markov Model Based Classifiers, 22
1.5.4 k-Nearest Neighbor Algorithm, 22
1.5.5 Naïve Bayes Classifier, 23
vu
viii
CONTENTS
1.6 Multimodal Emotion Recognition, 24
1.7 Stimulus Generation for Emotion Arousal, 24
1.8 Validation Techniques, 26
1.8.1 Performance Metrics for Emotion Classification, 27
1.9 Summary, 27 References, 28 Author Biographies, 44
2 Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition
Van Tong and Qiang Ji
2.1 Introduction, 48
2.2 Related Work, 49
2.3 Modeling the Semantic and Dynamic Relationships Among AUs With a DBN, 50
2.3.1 A DBN for Modeling Dynamic Dependencies among AUs, 51
2.3.2 Constructing the Initial DBN, 54
2.3.3 Learning DBN Model, 55
2.3.4 AU Recognition Through DBN Inference, 59
2.4 Experimental Results, 60
2.4.1 Facial Action Unit Databases, 60
2.4.2 Evaluation on Cohn and Kanade Database, 61
2.4.3 Evaluation on Spontaneous Facial Expression Database, 62
2.5 Conclusion, 64 References, 64 Author Biographies, 66
3 Facial Expressions: A Cross-Cultural Study
Chandrani Saha, Washef Ahmed, Soma Mitra, Debasis Mazumdar, and Sushmita Mitra
3.1 Introduction, 69
3.2 Extraction of Facial Regions and Ekman’s Action Units, 71
3.2.1 Computation of Optical Flow Vector Representing Muscle Movement, 72
3.2.2 Computation of Region of Interest, 73
3.2.3 Computation of Feature Vectors Within ROL 74
3.2.4 Facial Deformation and Ekman's Action Units, 75
3.3 Cultural Variation in Occurrence of Different AUs, 76
3.4 Classification Performance Considering Cultural Variability, 79
3.5 Conclusion, 84 References, 84 Author Biographies, 86
CONTENTS
lx
4 A Subject-Dependent Facial Expression Recognition System 89
Chuan-Yu Chang and Yan-Chiang Huang
4.1 Introduction, 89
4.2 Proposed Method, 91
4.2.1 Face Detection, 91
4.2.2 Preprocessing, 92
4.2.3 Facial Feature Extraction, 95
4.2.4 Face Recognition, 98
4.2.5 Facial Expression Recognition, 99
4.3 Experiment Result, 103
4.3.1 Parameter Determination of the RBFNN, 105
4.3.2 Comparison of Facial Features, 107
4.3.3 Comparison of Face Recognition Using “Inner Face” and Full Face, 108
4.3.4 Comparison of Subject-Dependent and Subject-Independent Facial Expression Recognition Systems, 108
4.3.5 Comparison with Other Approaches, 109
4.4 Conclusion, 109 Acknowledgment, 110 References, 110 Author Biographies, 112
5 Facial Expression Recognition Using Independent Component
Features and Hidden Markov Model 113
Md. Zia Uddin and Tae-Seong Kim
5.1 Introduction, 114
5.2 Methodology, 115
5.2.1 Expression Image Preprocessing, 115
5.2.2 Feature Extraction, 116
5.2.3 Codebook and Code Generation, 121
5.2.4 Expression Modeling and Training Using HMM, 121
5.3 Experimental Results, 123
5.4 Conclusion, 125 Acknowledgments, 125 References, 126 Author Biographies, 127
6 Feature Selection for Facial Expression Based on Rough Set Theory 129
Yang Yang and Guoyin Wang
6.1 Introduction, 129
6.2 Feature Selection for Emotion Recognition Based on Rough Set Theory, 131
6.2.1 Basic Concepts of Rough Set Theory, Í 31
X
CONTENTS
6.2.2 Feature Selection Based on Rough Set and Domain-Oriented Data-Driven Data Mining Theories, 133
6.2.3 Attribute Reduction for Emotion Recognition, 136
6.3 Experiment Results and Discussion, 137
6.3.1 Experiment Condition, 137
6.3.2 Experiments for Feature Selection Method for Emotion Recognition, 139
6.3.3 Experiments for the Features Concerning Mouth for Emotion Recognition, 141
6.4 Conclusion, 143 Acknowledgments, 143 References, 143 Author Biographies, 145
7 Emotion Recognition from Facial Expressions Using
Tvpe-2 Fuzzy Sets 147
Anisha Haider, Amit Konar Anma Chakra bony, and Atulya K, Nagar
7.1 Introduction, 148
7.2 Preliminaries on Type-2 Fuzzy Sets, 150
7.2.1 Type-2 Fuzzy Sets, 150
7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition, 152
7.3.1 Principles Used in the IT2FS Approach, 153
7.3.2 Principles Used in the GT2FS Approach, 155
7.3.3 Methodology, 156
7.4 Fuzzy Type-2 Membership Evaluation, 157
7.5 Experimental Details, 161
7.5.1 Feature Extraction, 161
7.5.2 Creating the Type-2 Fuzzy Face-Space, 164
7.5.3 Emotion Recognition of an Unknown Facial Expression, 165
7.6 Performance Analysis, 167
7.6.1 The McNemar’s Test, 169
7.6.2 Friedman Test, 171
7.6.3 The Confusion Matrix-Based RMS Error, 173
7.7 Conclusion, 175 References, 176 Author Biographies, 180
8 Emotion Recognition from Non-frontal Facial Images 183
Wenming Zheng, Hao Tang, and Thomas 5. Huang
8.1 introduction, 184
8.2 A Brief Review of Automatic Emotional Expression Recognition, 187
8.2.1 Framework of Automatic Facial Emotion Recognition System, 187
8.2.2 Extraction of Geometric Features, 189
8.2.3 Extraction of Appearance Features, 190
CONTENTS
xi
8.3 Databases for Non-frontal Facial Emotion Recognition, 191
8.3.1 BU-3DFE Database, 192
8.3.2 BU-4DFE Database, 194
8.3.3 CMU Multi-PIE Database, 195
8.3.4 Bosphorus 3D Database, 195
8.4 Recent Advances of Emotion Recognition from Non-Frontal Facial Images, 196
8.4.1 Emotion Recognition from 3D Facial Models, 196
8.4.2 Emotion Recognition from Non-frontal 2D Facial Images, 197
8.5 Discussions and Conclusions, 205 Acknowledgments, 206 References, 206
Author Biographies, 211
9 Maximum a Posteriori Based Fusion Method for Speech
Emotion Recognition 215
Ling Cen, Zhu Liang Yu, and Wee Ser
9.1 Introduction, 216
9.2 Acoustic Feature Extraction for Emotion Recognition, 219
9.3 Proposed Map-Based Fusion Method, 223
9.3.1 Base Classifiers, 224
9.3.2 MAP-Based Fusion, 225
9.3.3 Addressing Small Training Dataset Problem—Calculation
o(fclcL(crl 226
9.3.4 Training and Testing Procedure, 228
9.4 Experiment, 229
9.4.1 Database, 229
9.4.2 Experiment Description, 229
9.4.3 Results and Discussion, 230
9.5 Conclusion, 232 References, 232 Author Biographies, 234
10 Emotion Recognition in Naturalistic Speech and
Language—A Survey 237
Felix Wen ingen Martin Wöllmen and Björn Schuller
10.1 Introduction, 238
10.2 Tasks and Applications, 239
10.2.1 Use-Cases for Automatic Emotion Recognition from Speech and Language, 239
10.2.2 Databases, 241
10.2.3 Modeling and Annotation: Categories versus Dimensions, 242
10.2.4 Unit of Analysis, 243
CONTENTS
• * XII
10.3 Implementation and Evaluation. 244
10.3.1 Feature Extraction, 245
10.3.2 Feature and Instance Selection, 247
10.3.3 Classification and Learning, 248
10.3.4 Partitioning and Evaluation, 250
10.3.5 Research Toolkits and Open-Source Software, 252
10.4 Challenges, 253
10.4.1 Non-prototypicality, Reliability, and Class Sparsity, 253
10.4.2 Generalization, 255
10.4.3 Real-Time Processing, 256
10.4.4 Acoustic Environments: Noise and Reverberation, 256
10.5 Conclusion and Outlook, 257 Acknowledgment, 259 References, 259
Author Biographies, 267
11 EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques
Pcmagiotis C. Petrantonakis and Leontios J. Hadjileontladis
11.1 Introduction, 270
11.2 Brain Activity and Emotions, 271
11.3 EEG-ER Systems: An Overview, 272
11.4 Emotion Elicitation, 273
11.4.1 Discrete Emotions, 273
11.4.2 Affective States, 274
11.4.3 Datasets, 274
11.5 Advanced Signal Processing in EEG-ER, 275
11.5.1 Discrete Emotions, 275
11.5.2 Affective States, 280
11.6 Concluding Remarks and Future Directions, 287 References, 289
Author Biographies, 292
12 Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT
M. Murugappan
12.1 Introduction, 296
12.2 Related Work, 297
12.3 Research Methodology, 299
12.3.1 Physiological Signals Acquisition, 299
12.3.2 Preprocessing and Normalization, 302
12.3.3 Feature Extraction, 303
12.3.4 Emotion Classification, 305
CONTENTS
xili
12.4 Experimental Results and Discussions, 306
12.5 Conclusion, 310
12.6 Future Work, 310 Acknowledgments, 310 References, 310 Author Biography, 312
13 Toward Affective Brain-Computer Interface: Fundamentals and
Analysis of EEG-Based Emotion Classification 315
Yuan-Pin Lin, Tzyy-Ping Jung, Yijun Wang, and Julie Onton
13.1 Introduction, 316
13.1.1 Brain-Computer Interface, 316
13.1.2 EEG Dynamics Associated with Emotion, 317
13.1.3 Current Research in EEG-Based Emotion Classification, 319
13.1.4 Addressed Issues, 322
13.2 Materials and Methods, 323
13.2.1 EEG Dataset, 323
13.2.2 EEG Feature Extraction, 323
13.2.3 EEG Feature Selection, 325
13.2.4 EEG Feature Classification, 325
13.3 Results and Discussion, 327
13.3.1 Superiority of Differential Power Asymmetry, 327
13.3.2 Gender Independence in Differential Power Asymmetry, 328
13.3.3 Channel Reduction from Differential Power Asymmetry, 330
13.3.4 Generalization of Differential Power Asymmetry, 331
13.4 Conclusion, 332
13.5 Issues and Challenges Toward ABCIs, 332
13.5.1 Directions for Improving Estimation Performance, 333
13.5.2 Online System Implementation, 334 Acknowledgments, 336
References, 336 Author Biographies, 340
14 Bodily Expression for Automatic Affect Recognition 343
Hatice Gunes, Caifeng Shan, Shizhi Chen, and YingLi Tian
14.1 Introduction, 344
14.2 Background and Related Work, 345
14.2.1 Body as an Autonomous Channel for Affect Perception and Analysis, 346
14.2.2 Body as an Additional Channel for Affect Perception and Analysis, 350
14.2.3 Bodily Expression Data and Annotation, 352
14.3 Creating a Database of Facial and Bodily Expressions: The FABO Database, 353
xiv
CONTENTS
14.4 Automatic Recognition of Affect from Bodily Expressions, 356 14.4. i Body as an Autonomous Channel for Affect Analysis, 356
14.4.2 Body as an Additional Channel for Affect Analysis, 358
14.5 Automatic Recognition of Bodily Expression Temporal Dynamics, 361
14.5.1 Feature Extraction, 362
14.5.2 Feature Representation and Combination, 364
14.5.3 Experiments, 365
14.6 Discussion and Outlook, 367
14.7 Conclusions, 369 Acknowledgments, 370 References, 370 Author Biographies, 375
15 Building a Robust System for Multimodal Emotion Recognition 379
Johannes Wagner, Florian Lingenfelsen and Elisabeth André
15.1 Introduction, 380
15.2 Related Work, 381
15.3 The Calias Expressivity Corpus, 382
15.3. Ï Segmentation of Data, 383
15.3.2 Emotion Modeling, 383
15.3.3 Annotation, 384
15.4 Methodology, 386
15.4.1 Classification Model, 386
15.4.2 Feature Extraction, 387
15.4.3 Speech Features, 387
15.4.4 Facial Features, 389
15.4.5 Feature Selection, 389
15.4.6 Recognizing Missing Data, 390
15.5 Multisensor Data Fusion, 390
15.5.1 Feature-Level Fusion, 390
15.5.2 Ensemble-Based Systems and Decision-Level Fusion, 391
15.6 Experiments, 395
15.6.1 Evaluation Method, 396
15.6.2 Results, 396
15.6.3 Discussion, 397
15.6.4 Contradictory Cues, 397
15.7 Online Recognition System, 399
15.7.1 Social Signal Interpretation, 399
15.7.2 Synchronized Data Recording and Annotation, 400
15.7.3 Feature Extraction and Model Training, 401
15.7.4 Online Classification, 401
15.8 Conclusion, 403 Acknowledgment, 404 References, 404 Author Biographies, 4 i0
16 Semantic Audiovisual Data Fusion for Automatic Emotion Recognition
Dragos Datcu and Leon J. M. Rothkrantz
CONTENTS
xv
411
16.1 Introduction, 412
16.2 Related Work, 413
16.3 Data Set Preparation, 416
16.4 Architecture, 418
16.4.1 Classification Model ,418
16.4.2 Emotion Estimation from Speech, 419
16.4.3 Video Analysis, 420
16.4.4 Fusion Model, 428
16.5 Results, 431
16.6 Conclusion, 432 References, 432 Author Biographies, 434
17 A Multilevel Fusion Approach for Audiovisual
Emotion Recognition 437
Girija Chetty, Michael Wagner, and Roland Goecke
17.1 Introduction, 437
17.2 Motivation and Background, 438
17.3 Facial Expression Quantification, 440
17.4 Experiment Design, 444
17.4.1 Data Corpora, 444
17.4.2 Facial Deformation Features, 445
17.4.3 Marker-Based Audio Visual Features, 447
17.4.4 Expression Classification and Multilevel Fusion, 448
17.5 Experimental Results and Discussion, 450
17.5.1 Facial Expression Quantification, 450
17.5.2 Facial Expression Classification Using SVDF and VDF Features, 451
17.5.3 Audiovisual Fusion Experiments, 451
17.6 Conclusion, 456 References, 456 Author Biographies, 459
18 From a Discrete Perspective of Emotions to Continuous, Dynamic,
and Multimodal Affect Sensing 461
Isabelle Hupont, Sergio Ballano, Eva Cerezo, and Sandra Baldassarri
18.1 Introduction, 462
18.2 A Novel Method for Discrete Emotional Classification of Facial Images, 465
18.2.1 Selection and Extraction of Facial Inputs, 465
xvf
CONTENTS
18.2.2 Classifiers Selection and Combination, 467
18.2.3 Results. 468
18.3 A 2D Emotional Space for Continuous and Dynamic Facial Affect Sensing. 469
18.3.1 Facial Expressions Mapping to the Whissell Affective Space, 469
18.3.2 From Still images to Video Sequences through 2D Emotional Kinematics Modeling, 473
18.4 Expansion to Multimodal Affect Sensing, 474
18.4.1 Step 1: 2D Emotional Mapping to the Whissell Space, 477
18.4.2 Step 2: Temporal Fusion of Individual Modalities to Obtain a Continuous 2D Emotional Path, 477
18.4.3 Step 3: “Emotional Kinematics" Path Filtering, 478
18.5 Building Tools That Care. 479
18.5. i T-EDUCO: A T-learning Tutoring Tool. 479
18.5.2 Multimodal Fusion Application to Instant Messaging, 482
18.6 Concluding Remarks and Future Work, 486 Acknowledgments, 488
References, 488 Author Biographies, 491
19 Audiovisual Emotion Recognition Using Semi-Coupled Hidden
Markov Model with State-Based Alignment Strategy 493
Clumg-Hsien Wit, Jen-Chun IJn„ and Wen-Id Wei
19.1 Introduction, 494
19.2 Feature Extraction, 495
19.2.1 Facial Feature Extraction, 496
19.2.2 Prosodic Feature Extraction, 498
19.3 Semi-Coupled Hidden Markov Model, 500
19.3.1 Model Formulation, 500
19.3.2 State-Based Bimodal Alignment Strategy, 502
19.4 Experiments, 504
19.4.1 Data Collection, 504
19.4.2 Experimental Results, 506
19.5 Conclusion, 508 References, 509 Author Biographies, 512
20 Emotion Recognition in Car Industry 515
Christos fX Katsis, George Rigas, Yorgos Goietsis, and Dimitrios /. Fotiadis
20.1 introduction, 516
20.2 An Overview of Application for the Car Industry, 517
20.3 Modality-Based Categorization, 517
20.3.1 Video-Image-Based Emotion Recognition, 518
20.3.2 Speech Based Emotion Recognition, 518
CONTENTS
xvii
20.3.3 Biosignal-Based Emotion Recognition, 519
20.3.4 Multimodal Based Emotion Recognition, 519
20.4 Emotion-Based Categorization, 520
20.4.1 Stress, 520
20.4.2 Fatigue, 521
20.4.3 Confusion and Nervousness, 522
20.4.4 Distraction, 522
20.5 Two Exemplar Cases, 523
20.5.1 AUBADE, 523
20.5.2 I-Way, 530
20.5.3 Results, 535
20.6 Open Issues and Future Steps, 536
20.7 Conclusion, 537 References, 537 Author Biographies, 543
Index
545 |
| any_adam_object | 1 |
| author2 | Konar, Amit 1963- Chakraborty, Aruna 1977- |
| author2_role | edt edt |
| author2_variant | a k ak a c ac |
| author_GND | (DE-588)1063311365 (DE-588)1100938923 |
| author_facet | Konar, Amit 1963- Chakraborty, Aruna 1977- |
| building | Verbundindex |
| bvnumber | BV043026565 |
| classification_rvk | CP 3200 ST 330 |
| ctrlnum | (OCoLC)906005233 (DE-599)BVBBV043026565 |
| dewey-full | 004.01/9 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 004 - Computer science |
| dewey-raw | 004.01/9 |
| dewey-search | 004.01/9 |
| dewey-sort | 14.01 19 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik Psychologie |
| format | Book |
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| genre | (DE-588)4143413-4 Aufsatzsammlung gnd-content |
| genre_facet | Aufsatzsammlung |
| id | DE-604.BV043026565 |
| illustrated | Illustrated |
| indexdate | 2024-10-23T10:06:12Z |
| institution | BVB |
| isbn | 9781118130667 9781118910603 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-028451220 |
| oclc_num | 906005233 |
| open_access_boolean | |
| owner | DE-384 DE-11 DE-355 DE-BY-UBR |
| owner_facet | DE-384 DE-11 DE-355 DE-BY-UBR |
| physical | xxxi, 548 Seiten Illustrationen, Diagramme |
| publishDate | 2015 |
| publishDateSearch | 2015 |
| publishDateSort | 2015 |
| publisher | Wiley |
| record_format | marc |
| spelling | Emotion recognition a pattern analysis approach edited by Amit Konar (Artificial Intelligence Laboratory, Department of Electronics and Telecommuniation Engineering, Jadavpur University, Kolkata, India), Aruna Chakraborty (Department of Computer Science & Engineering, St. Thomas' College of Engineering & Technology, Kolkata, India) Hoboken, New Jersey Wiley [2015] © 2015 xxxi, 548 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Human-computer interaction Artificial intelligence Emotions / Computer simulation Pattern recognition systems Context-aware computing TECHNOLOGY & ENGINEERING / Electronics / Digital bisacsh COMPUTERS / Computer Vision & Pattern Recognition bisacsh Artificial intelligence fast Context-aware computing fast Emotions / Computer simulation fast Human-computer interaction fast Künstliche Intelligenz Gefühl (DE-588)4019702-5 gnd rswk-swf Mustererkennung (DE-588)4040936-3 gnd rswk-swf (DE-588)4143413-4 Aufsatzsammlung gnd-content Gefühl (DE-588)4019702-5 s Mustererkennung (DE-588)4040936-3 s DE-604 Konar, Amit 1963- (DE-588)1063311365 edt Chakraborty, Aruna 1977- (DE-588)1100938923 edt Erscheint auch als Online-Ausgabe 978-1-11-891061-0 Digitalisierung UB Regensburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028451220&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Emotion recognition a pattern analysis approach Human-computer interaction Artificial intelligence Emotions / Computer simulation Pattern recognition systems Context-aware computing TECHNOLOGY & ENGINEERING / Electronics / Digital bisacsh COMPUTERS / Computer Vision & Pattern Recognition bisacsh Artificial intelligence fast Context-aware computing fast Emotions / Computer simulation fast Human-computer interaction fast Künstliche Intelligenz Gefühl (DE-588)4019702-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| subject_GND | (DE-588)4019702-5 (DE-588)4040936-3 (DE-588)4143413-4 |
| title | Emotion recognition a pattern analysis approach |
| title_auth | Emotion recognition a pattern analysis approach |
| title_exact_search | Emotion recognition a pattern analysis approach |
| title_full | Emotion recognition a pattern analysis approach edited by Amit Konar (Artificial Intelligence Laboratory, Department of Electronics and Telecommuniation Engineering, Jadavpur University, Kolkata, India), Aruna Chakraborty (Department of Computer Science & Engineering, St. Thomas' College of Engineering & Technology, Kolkata, India) |
| title_fullStr | Emotion recognition a pattern analysis approach edited by Amit Konar (Artificial Intelligence Laboratory, Department of Electronics and Telecommuniation Engineering, Jadavpur University, Kolkata, India), Aruna Chakraborty (Department of Computer Science & Engineering, St. Thomas' College of Engineering & Technology, Kolkata, India) |
| title_full_unstemmed | Emotion recognition a pattern analysis approach edited by Amit Konar (Artificial Intelligence Laboratory, Department of Electronics and Telecommuniation Engineering, Jadavpur University, Kolkata, India), Aruna Chakraborty (Department of Computer Science & Engineering, St. Thomas' College of Engineering & Technology, Kolkata, India) |
| title_short | Emotion recognition |
| title_sort | emotion recognition a pattern analysis approach |
| title_sub | a pattern analysis approach |
| topic | Human-computer interaction Artificial intelligence Emotions / Computer simulation Pattern recognition systems Context-aware computing TECHNOLOGY & ENGINEERING / Electronics / Digital bisacsh COMPUTERS / Computer Vision & Pattern Recognition bisacsh Artificial intelligence fast Context-aware computing fast Emotions / Computer simulation fast Human-computer interaction fast Künstliche Intelligenz Gefühl (DE-588)4019702-5 gnd Mustererkennung (DE-588)4040936-3 gnd |
| topic_facet | Human-computer interaction Artificial intelligence Emotions / Computer simulation Pattern recognition systems Context-aware computing TECHNOLOGY & ENGINEERING / Electronics / Digital COMPUTERS / Computer Vision & Pattern Recognition Künstliche Intelligenz Gefühl Mustererkennung Aufsatzsammlung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=028451220&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT konaramit emotionrecognitionapatternanalysisapproach AT chakrabortyaruna emotionrecognitionapatternanalysisapproach |