{"product_id":"emotion-recognition-isbn-9781118130667","title":"Emotion Recognition","description":"\u003cp\u003e\u003cb\u003eA timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003eThis book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.\u003cbr\u003e\u003cbr\u003eWritten by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.\u003cbr\u003e\u003cbr\u003eThere is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eEmotion Recognition: A Pattern Analysis Approach\u003c\/i\u003e would be of great interest to researchers, graduate students and practitioners, as the book\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOffers both foundations and advances on emotion recognition in a single volume\u003c\/li\u003e \u003cli\u003eProvides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains\u003c\/li\u003e \u003cli\u003eInspires young researchers to prepare themselves for their own research\u003c\/li\u003e \u003cli\u003eDemonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.\u003c\/li\u003e \u003c\/ul\u003e  \u003cp\u003ePreface xix\u003c\/p\u003e \u003cp\u003eAcknowledgments xxvii\u003c\/p\u003e \u003cp\u003eContributors xxix\u003c\/p\u003e \u003cp\u003e\u003cb\u003e1 Introduction to Emotion Recognition 1\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAmit Konar, Anisha Halder, and Aruna Chakraborty\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Basics of Pattern Recognition, 1\u003c\/p\u003e \u003cp\u003e1.2 Emotion Detection as a Pattern Recognition Problem, 2\u003c\/p\u003e \u003cp\u003e1.3 Feature Extraction, 3\u003c\/p\u003e \u003cp\u003e1.4 Feature Reduction Techniques, 15\u003c\/p\u003e \u003cp\u003e1.5 Emotion Classification, 17\u003c\/p\u003e \u003cp\u003e1.6 Multimodal Emotion Recognition, 24\u003c\/p\u003e \u003cp\u003e1.7 Stimulus Generation for Emotion Arousal, 24\u003c\/p\u003e \u003cp\u003e1.8 Validation Techniques, 26\u003c\/p\u003e \u003cp\u003e1.9 Summary, 27\u003c\/p\u003e \u003cp\u003eReferences, 28\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 44\u003c\/p\u003e \u003cp\u003e\u003cb\u003e2 Exploiting Dynamic Dependencies Among Action Units for Spontaneous Facial Action Recognition 47\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYan Tong and Qiang Ji\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction, 48\u003c\/p\u003e \u003cp\u003e2.2 Related Work, 49\u003c\/p\u003e \u003cp\u003e2.3 Modeling the Semantic and Dynamic Relationships Among AUs With a DBN, 50\u003c\/p\u003e \u003cp\u003e2.4 Experimental Results, 60\u003c\/p\u003e \u003cp\u003e2.5 Conclusion, 64\u003c\/p\u003e \u003cp\u003eReferences, 64\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 66\u003c\/p\u003e \u003cp\u003e\u003cb\u003e3 Facial Expressions: A Cross-Cultural Study 69\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eChandrani Saha, Washef Ahmed, Soma Mitra, Debasis Mazumdar, and Sushmita Mitra\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction, 69\u003c\/p\u003e \u003cp\u003e3.2 Extraction of Facial Regions and Ekman’s Action Units, 71\u003c\/p\u003e \u003cp\u003e3.3 Cultural Variation in Occurrence of Different AUs, 76\u003c\/p\u003e \u003cp\u003e3.4 Classification Performance Considering Cultural Variability, 79\u003c\/p\u003e \u003cp\u003e3.5 Conclusion, 84\u003c\/p\u003e \u003cp\u003eReferences, 84\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 86\u003c\/p\u003e \u003cp\u003e\u003cb\u003e4 A Subject-Dependent Facial Expression Recognition System 89\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eChuan-Yu Chang and Yan-Chiang Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Introduction, 89\u003c\/p\u003e \u003cp\u003e4.2 Proposed Method, 91\u003c\/p\u003e \u003cp\u003e4.3 Experiment Result, 103\u003c\/p\u003e \u003cp\u003e4.4 Conclusion, 109\u003c\/p\u003e \u003cp\u003eAcknowledgment, 110\u003c\/p\u003e \u003cp\u003eReferences, 110\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 112\u003c\/p\u003e \u003cp\u003e\u003cb\u003e5 Facial Expression Recognition Using Independent Component Features and Hidden Markov Model 113\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eMd. Zia Uddin and Tae-Seong Kim\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction, 114\u003c\/p\u003e \u003cp\u003e5.2 Methodology, 115\u003c\/p\u003e \u003cp\u003e5.3 Experimental Results, 123\u003c\/p\u003e \u003cp\u003e5.4 Conclusion, 125\u003c\/p\u003e \u003cp\u003eAcknowledgments, 125\u003c\/p\u003e \u003cp\u003eReferences, 126\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 127\u003c\/p\u003e \u003cp\u003e\u003cb\u003e6 Feature Selection for Facial Expression Based on Rough Set Theory 129\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYong Yang and Guoyin Wang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction, 129\u003c\/p\u003e \u003cp\u003e6.2 Feature Selection for Emotion Recognition Based on Rough Set Theory, 131\u003c\/p\u003e \u003cp\u003e6.3 Experiment Results and Discussion, 137\u003c\/p\u003e \u003cp\u003e6.4 Conclusion, 143\u003c\/p\u003e \u003cp\u003eAcknowledgments, 143\u003c\/p\u003e \u003cp\u003eReferences, 143\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 145\u003c\/p\u003e \u003cp\u003e\u003cb\u003e7 Emotion Recognition from Facial Expressions Using Type-2 Fuzzy Sets 147\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eAnisha Halder, Amit Konar, Aruna Chakraborty, and Atulya K. Nagar\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction, 148\u003c\/p\u003e \u003cp\u003e7.2 Preliminaries on Type-2 Fuzzy Sets, 150\u003c\/p\u003e \u003cp\u003e7.3 Uncertainty Management in Fuzzy-Space for Emotion Recognition, 152\u003c\/p\u003e \u003cp\u003e7.4 Fuzzy Type-2 Membership Evaluation, 157\u003c\/p\u003e \u003cp\u003e7.5 Experimental Details, 161\u003c\/p\u003e \u003cp\u003e7.6 Performance Analysis, 167\u003c\/p\u003e \u003cp\u003e7.7 Conclusion, 175\u003c\/p\u003e \u003cp\u003eReferences, 176\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 180\u003c\/p\u003e \u003cp\u003e\u003cb\u003e8 Emotion Recognition from Non-frontal Facial Images 183\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eWenming Zheng, Hao Tang, and Thomas S. Huang\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction, 184\u003c\/p\u003e \u003cp\u003e8.2 A Brief Review of Automatic Emotional Expression Recognition, 187\u003c\/p\u003e \u003cp\u003e8.3 Databases for Non-frontal Facial Emotion Recognition, 191\u003c\/p\u003e \u003cp\u003e8.4 Recent Advances of Emotion Recognition from Non-Frontal Facial Images, 196\u003c\/p\u003e \u003cp\u003e8.5 Discussions and Conclusions, 205\u003c\/p\u003e \u003cp\u003eAcknowledgments, 206\u003c\/p\u003e \u003cp\u003eReferences, 206\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003e9 Maximum a Posteriori Based Fusion Method for Speech Emotion Recognition 215\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eLing Cen, Zhu Liang Yu, and Wee Ser\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction, 216\u003c\/p\u003e \u003cp\u003e9.2 Acoustic Feature Extraction for Emotion Recognition, 219\u003c\/p\u003e \u003cp\u003e9.3 Proposed Map-Based Fusion Method, 223\u003c\/p\u003e \u003cp\u003e9.4 Experiment, 229\u003c\/p\u003e \u003cp\u003e9.5 Conclusion, 232\u003c\/p\u003e \u003cp\u003eReferences, 232\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 234\u003c\/p\u003e \u003cp\u003e\u003cb\u003e10 Emotion Recognition in Naturalistic Speech and Language—A Survey 237\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eFelix Weninger, Martin W¨ollmer, and Björn Schuller\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction, 238\u003c\/p\u003e \u003cp\u003e10.2 Tasks and Applications, 239\u003c\/p\u003e \u003cp\u003e10.3 Implementation and Evaluation, 244\u003c\/p\u003e \u003cp\u003e10.4 Challenges, 253\u003c\/p\u003e \u003cp\u003e10.5 Conclusion and Outlook, 257\u003c\/p\u003e \u003cp\u003eAcknowledgment, 259\u003c\/p\u003e \u003cp\u003eReferences, 259\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 267\u003c\/p\u003e \u003cp\u003e\u003cb\u003e11 EEG-Based Emotion Recognition Using Advanced Signal Processing Techniques 269\u003c\/b\u003e\u003cbr\u003e \u003ci\u003ePanagiotis C. Petrantonakis and Leontios J. Hadjileontiadis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction, 270\u003c\/p\u003e \u003cp\u003e11.2 Brain Activity and Emotions, 271\u003c\/p\u003e \u003cp\u003e11.3 EEG-ER Systems: An Overview, 272\u003c\/p\u003e \u003cp\u003e11.4 Emotion Elicitation, 273\u003c\/p\u003e \u003cp\u003e11.5 Advanced Signal Processing in EEG-ER, 275\u003c\/p\u003e \u003cp\u003e11.6 Concluding Remarks and Future Directions, 287\u003c\/p\u003e \u003cp\u003eReferences, 289\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 292\u003c\/p\u003e \u003cp\u003e\u003cb\u003e12 Frequency Band Localization on Multiple Physiological Signals for Human Emotion Classification Using DWT 295\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eM. Murugappan\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Introduction, 296\u003c\/p\u003e \u003cp\u003e12.2 Related Work, 297\u003c\/p\u003e \u003cp\u003e12.3 Research Methodology, 299\u003c\/p\u003e \u003cp\u003e12.4 Experimental Results and Discussions, 306\u003c\/p\u003e \u003cp\u003e12.5 Conclusion, 310\u003c\/p\u003e \u003cp\u003e12.6 Future Work, 310\u003c\/p\u003e \u003cp\u003eAcknowledgments, 310\u003c\/p\u003e \u003cp\u003eReferences, 310\u003c\/p\u003e \u003cp\u003eAuthor Biography, 312\u003c\/p\u003e \u003cp\u003e\u003cb\u003e13 Toward Affective Brain–Computer Interface: Fundamentals and Analysis of EEG-Based Emotion Classification 315\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eYuan-Pin Lin, Tzyy-Ping Jung, Yijun Wang, and Julie Onton\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction, 316\u003c\/p\u003e \u003cp\u003e13.2 Materials and Methods, 323\u003c\/p\u003e \u003cp\u003e13.3 Results and Discussion, 327\u003c\/p\u003e \u003cp\u003e13.4 Conclusion, 332\u003c\/p\u003e \u003cp\u003e13.5 Issues and Challenges Toward ABCIs, 332\u003c\/p\u003e \u003cp\u003eAcknowledgments, 336\u003c\/p\u003e \u003cp\u003eReferences, 336\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 340\u003c\/p\u003e \u003cp\u003e\u003cb\u003e14 Bodily Expression for Automatic Affect Recognition 343\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eHatice Gunes, Caifeng Shan, Shizhi Chen, and YingLi Tian\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction, 344\u003c\/p\u003e \u003cp\u003e14.2 Background and Related Work, 345\u003c\/p\u003e \u003cp\u003e14.3 Creating a Database of Facial and Bodily Expressions: The FABO Database, 353\u003c\/p\u003e \u003cp\u003e14.4 Automatic Recognition of Affect from Bodily Expressions, 356\u003c\/p\u003e \u003cp\u003e14.5 Automatic Recognition of Bodily Expression Temporal Dynamics, 361\u003c\/p\u003e \u003cp\u003e14.6 Discussion and Outlook, 367\u003c\/p\u003e \u003cp\u003e14.7 Conclusions, 369\u003c\/p\u003e \u003cp\u003eAcknowledgments, 370\u003c\/p\u003e \u003cp\u003eReferences, 370\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 375\u003c\/p\u003e \u003cp\u003e\u003cb\u003e15 Building a Robust System for Multimodal Emotion Recognition 379\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eJohannes Wagner, Florian Lingenfelser, and Elisabeth André\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction, 380\u003c\/p\u003e \u003cp\u003e15.2 Related Work, 381\u003c\/p\u003e \u003cp\u003e15.3 The Callas Expressivity Corpus, 382\u003c\/p\u003e \u003cp\u003e15.4 Methodology, 386\u003c\/p\u003e \u003cp\u003e15.5 Multisensor Data Fusion, 390\u003c\/p\u003e \u003cp\u003e15.6 Experiments, 395\u003c\/p\u003e \u003cp\u003e15.7 Online Recognition System, 399\u003c\/p\u003e \u003cp\u003e15.8 Conclusion, 403\u003c\/p\u003e \u003cp\u003eAcknowledgment, 404\u003c\/p\u003e \u003cp\u003eReferences, 404\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 410\u003c\/p\u003e \u003cp\u003e\u003cb\u003e16 Semantic Audiovisual Data Fusion for Automatic Emotion Recognition 411\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eDragos Datcu and Leon J. M. Rothkrantz\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction, 412\u003c\/p\u003e \u003cp\u003e16.2 Related Work, 413\u003c\/p\u003e \u003cp\u003e16.3 Data Set Preparation, 416\u003c\/p\u003e \u003cp\u003e16.4 Architecture, 418\u003c\/p\u003e \u003cp\u003e16.5 Results, 431\u003c\/p\u003e \u003cp\u003e16.6 Conclusion, 432\u003c\/p\u003e \u003cp\u003eReferences, 432\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 434\u003c\/p\u003e \u003cp\u003e\u003cb\u003e17 A Multilevel Fusion Approach for Audiovisual Emotion Recognition 437\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eGirija Chetty, Michael Wagner, and Roland Goecke\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e17.1 Introduction, 437\u003c\/p\u003e \u003cp\u003e17.2 Motivation and Background, 438\u003c\/p\u003e \u003cp\u003e17.3 Facial Expression Quantification, 440\u003c\/p\u003e \u003cp\u003e17.4 Experiment Design, 444\u003c\/p\u003e \u003cp\u003e17.5 Experimental Results and Discussion, 450\u003c\/p\u003e \u003cp\u003e17.6 Conclusion, 456\u003c\/p\u003e \u003cp\u003eReferences, 456\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 459\u003c\/p\u003e \u003cp\u003e\u003cb\u003e18 From a Discrete Perspective of Emotions to Continuous, Dynamic, and Multimodal Affect Sensing 461\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eIsabelle Hupont, Sergio Ballano, Eva Cerezo, and Sandra Baldassarri\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e18.1 Introduction, 462\u003c\/p\u003e \u003cp\u003e18.2 A Novel Method for Discrete Emotional Classification of Facial Images, 465\u003c\/p\u003e \u003cp\u003e18.3 A 2D Emotional Space for Continuous and Dynamic Facial Affect Sensing, 469\u003c\/p\u003e \u003cp\u003e18.4 Expansion to Multimodal Affect Sensing, 474\u003c\/p\u003e \u003cp\u003e18.5 Building Tools That Care, 479\u003c\/p\u003e \u003cp\u003e18.6 Concluding Remarks and Future Work, 486\u003c\/p\u003e \u003cp\u003eAcknowledgments, 488\u003c\/p\u003e \u003cp\u003eReferences, 488\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 491\u003c\/p\u003e \u003cp\u003e\u003cb\u003e19 Audiovisual Emotion Recognition Using Semi-Coupled Hidden Markov Model with State-Based Alignment Strategy 493\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eChung-Hsien Wu, Jen-Chun Lin, and Wen-Li Wei\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e19.1 Introduction, 494\u003c\/p\u003e \u003cp\u003e19.2 Feature Extraction, 495\u003c\/p\u003e \u003cp\u003e19.3 Semi-Coupled Hidden Markov Model, 500\u003c\/p\u003e \u003cp\u003e19.4 Experiments, 504\u003c\/p\u003e \u003cp\u003e19.5 Conclusion, 508\u003c\/p\u003e \u003cp\u003eReferences, 509\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 512\u003c\/p\u003e \u003cp\u003e\u003cb\u003e20 Emotion Recognition in Car Industry 515\u003c\/b\u003e\u003cbr\u003e \u003ci\u003eChristos D. Katsis, George Rigas, Yorgos Goletsis, and Dimitrios I. Fotiadis\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e20.1 Introduction, 516\u003c\/p\u003e \u003cp\u003e20.2 An Overview of Application for the Car Industry, 517\u003c\/p\u003e \u003cp\u003e20.3 Modality-Based Categorization, 517\u003c\/p\u003e \u003cp\u003e20.4 Emotion-Based Categorization, 520\u003c\/p\u003e \u003cp\u003e20.5 Two Exemplar Cases, 523\u003c\/p\u003e \u003cp\u003e20.6 Open Issues and Future Steps, 536\u003c\/p\u003e \u003cp\u003e20.7 Conclusion, 537\u003c\/p\u003e \u003cp\u003eReferences, 537\u003c\/p\u003e \u003cp\u003eAuthor Biographies, 543\u003c\/p\u003e \u003cp\u003eIndex 545\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAmit Konar\u003c\/b\u003e is a Professor of Electronics and Tele-Communication Engineering, Jadavpur University, India, where he offers graduate-level courses on Artificial Intelligence and directs research in Cognitive Science, Robotics and Human-Computer Interfaces. Dr. Konar is the recipient of many prestigious grants and awards and is an author of 10 books and over 350 research publications. He offered consultancy services to Government and private industries. He served editorial services to many journals, including \u003ci\u003eIEEE Transactions on Systems, Man and Cybernetics (Part-A)\u003c\/i\u003e and \u003ci\u003eIEEE Transactions on Fuzzy Systems\u003c\/i\u003e.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eAruna Chakraborty\u003c\/b\u003e is an Associate Professor with the Department of Computer Science and Engineering, St. Thomas' College of Engineering and Technology, India. She is also a Visiting Faculty with Jadavpur University, where she offers graduate-level courses on Intelligent Automation and Robotics, and Cognitive Science. Her research interest includes human-computer interfaces, emotional intelligence and reasoning with fuzzy logic.\u003c\/p\u003e \u003cp\u003e\u003cb\u003eA timely book containing foundations and current research directions on emotion recognition by facial expression, voice, gesture and biopotential signals\u003c\/b\u003e\u003cb\u003e\u003cbr\u003e\u003cbr\u003e\u003c\/b\u003eThis book provides a comprehensive examination of the research methodology of different modalities of emotion recognition. Key topics of discussion include facial expression, voice and biopotential signal-based emotion recognition. Special emphasis is given to feature selection, feature reduction, classifier design and multi-modal fusion to improve performance of emotion-classifiers.\u003cbr\u003e\u003cbr\u003eWritten by several experts, the book includes several tools and techniques, including dynamic Bayesian networks, neural nets, hidden Markov model, rough sets, type-2 fuzzy sets, support vector machines and their applications in emotion recognition by different modalities. The book ends with a discussion on emotion recognition in automotive fields to determine stress and anger of the drivers, responsible for degradation of their performance and driving-ability.\u003cbr\u003e\u003cbr\u003eThere is an increasing demand of emotion recognition in diverse fields, including psycho-therapy, bio-medicine and security in government, public and private agencies. The importance of emotion recognition has been given priority by industries including Hewlett Packard in the design and development of the next generation human-computer interface (HCI) systems.\u003cbr\u003e\u003cbr\u003e\u003ci\u003eEmotion Recognition: A Pattern Analysis Approach\u003c\/i\u003e would be of great interest to researchers, graduate students and practitioners, as the book\u003cbr\u003e\u003cbr\u003e\u003c\/p\u003e \u003cul\u003e \u003cli\u003eOffers both foundations and advances on emotion recognition in a single volume\u003c\/li\u003e \u003cli\u003eProvides a thorough and insightful introduction to the subject by utilizing computational tools of diverse domains\u003c\/li\u003e \u003cli\u003eInspires young researchers to prepare themselves for their own research\u003c\/li\u003e \u003cli\u003eDemonstrates direction of future research through new technologies, such as Microsoft Kinect, EEG systems etc.\u003c\/li\u003e \u003c\/ul\u003e \u003cb\u003e\u003cbr\u003eAmit Konar\u003c\/b\u003e is a Professor of Electronics and Tele-Communication Engineering, Jadavpur University, India, where he offers graduate-level courses on Artificial Intelligence and directs research in Cognitive Science, Robotics and Human-Computer Interfaces. Dr. Konar is the recipient of many prestigious grants and awards and is an author of 10 books and over 350 research publications. He offered consultancy services to Government and private industries. He served editorial services to many journals, including IEEE Transactions on Systems, Man and Cybernetics (Part-A) and IEEE Transactions on Fuzzy Systems.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAruna Chakraborty\u003c\/b\u003e is an Associate Professor with the Department of Computer Science and Engineering, St. Thomas' College of Engineering and Technology, India. She is also a Visiting Faculty with Jadavpur University, where she offers graduate-level courses on Intelligent Automation and Robotics, and Cognitive Science. Her research interest includes human-computer interfaces, emotional intelligence and reasoning with fuzzy logic.\u003cbr\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989124104421,"sku":"NP9781118130667","price":151.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781118130667.jpg?v=1761782892","url":"https:\/\/k12savings.com\/es\/products\/emotion-recognition-isbn-9781118130667","provider":"K12savings","version":"1.0","type":"link"}