Multimodal Affective Computing
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Multimodal Affective Computing
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Author : Ramón Zatarain Cabada
language : en
Publisher: Springer Nature
Release Date : 2023-06-26
Multimodal Affective Computing written by Ramón Zatarain Cabada and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-26 with Computers categories.
This book explores AI methodologies for the implementation of affective states in intelligent learning environments. Divided into four parts, Multimodal Affective Computing: Technologies and Applications in Learning Environments begins with an overview of Affective Computing and Intelligent Learning Environments, from their fundamentals and essential theoretical support up to their fusion and some successful practical applications. The basic concepts of Affective Computing, Machine Learning, and Pattern Recognition in Affective Computing, and Affective Learning Environments are presented in a comprehensive and easy-to-read manner. In the second part, a review on the emerging field of Sentiment Analysis for Learning Environments is introduced, including a systematic descriptive tour through topics such as building resources for sentiment detection, methods for data representation, designing and testing the classification models, and model integration into a learning system. The methodologies corresponding to Multimodal Recognition of Learning-Oriented Emotions are presented in the third part of the book, where topics such as building resources for emotion detection, methods for data representation, multimodal recognition systems, and multimodal emotion recognition in learning environments are presented. The fourth and last part of the book is devoted to a wide application field of the combination of methodologies, such as Automatic Personality Recognition, dealing with issues such as building resources for personality recognition, methods for data representation, personality recognition models, and multimodal personality recognition for affective computing. This book can be very useful not only for beginners who are interested in affective computing and intelligent learning environments, but also for advanced and experts in the practice and developments of the field. It complies an end-to-end treatment on these subjects, especially with educational applications, making it easy for researchers and students to get on track with fundamentals, established methodologies, conventional evaluation protocols, and the latest progress on these subjects.
Multimodal Affective Computing Affective Information Representation Modelling And Analysis
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Author : Gyanendra K. Verma
language : en
Publisher: Bentham Science Publishers
Release Date : 2023-03-21
Multimodal Affective Computing Affective Information Representation Modelling And Analysis written by Gyanendra K. Verma and has been published by Bentham Science Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-21 with Computers categories.
Affective computing is an emerging field situated at the intersection of artificial intelligence and behavioral science. Affective computing refers to studying and developing systems that recognize, interpret, process, and simulate human emotions. It has recently seen significant advances from exploratory studies to real-world applications. Multimodal Affective Computing offers readers a concise overview of the state-of-the-art and emerging themes in affective computing, including a comprehensive review of the existing approaches in applied affective computing systems and social signal processing. It covers affective facial expression and recognition, affective body expression and recognition, affective speech processing, affective text, and dialogue processing, recognizing affect using physiological measures, computational models of emotion and theoretical foundations, and affective sound and music processing. This book identifies future directions for the field and summarizes a set of guidelines for developing next-generation affective computing systems that are effective, safe, and human-centered.The book is an informative resource for academicians, professionals, researchers, and students at engineering and medical institutions working in the areas of applied affective computing, sentiment analysis, and emotion recognition.
The Oxford Handbook Of Affective Computing
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Author : Rafael A. Calvo
language : en
Publisher:
Release Date : 2015
The Oxford Handbook Of Affective Computing written by Rafael A. Calvo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with Computers categories.
The Oxford Handbook of Affective Computing is the definitive reference for research in Affective Computing (AC), a growing multidisciplinary field encompassing computer science, engineering, psychology, education, neuroscience, and many other disciplines. The handbook explores how affective factors influence interactions between humans and technology, how affect sensing and affect generation techniques can inform our understanding of human affect, and on the design, implementation, and evaluation of systems that intricately involve affect at their core. Suitable for use as a textbook in undergraduate or graduate courses in AC, the volume is a valuable resource for students, researchers, and practitioners worldwide.
Affective Computing And Intelligent Interaction
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Author : Jianhua Tao
language : en
Publisher: Springer Science & Business Media
Release Date : 2005-10-27
Affective Computing And Intelligent Interaction written by Jianhua Tao and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-27 with Computers categories.
This book constitutes the refereed proceedings of the First International Conference on Affective Computing and Intelligent Interaction, ACII 2005, held in Beijing, China in October 2005 as an associated event of ICCV 2005, the International Conference on Computer Vision. The 45 revised full papers and 81 revised poster papers presented were carefully reviewed and selected from 198 submissions. They cover a wide range of topics, such as facial expression recognition, face animation, emotional speech synthesis, intelligent agent, and virtual reality. The papers are organized in topical sections on affective face and gesture processing, affective speech processing, evaluation of affective expressivity, affective database, annotation and tools, psychology and cognition of affect, and affective interaction and systems and applications.
Affective Computing For Social Good
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Author : Muskan Garg
language : en
Publisher: Springer Nature
Release Date : 2024-10-07
Affective Computing For Social Good written by Muskan Garg and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-07 with Computers categories.
Affective Computing for Social Good: Enhancing Well-being, Empathy, and Equity offers an insightful journey into the intricate realm of affective computing. It covers a spectrum of topics ranging from foundational theories and technologies to ethical considerations and future possibilities. Beginning with "Deciphering the Emotional Spectrum: Advances in Emotion Science and Analysis," it sets the stage by tracing the evolution of understanding human emotions. Subsequent chapters explore practical applications, such as integrating clinical psychology with affective computing for therapeutic progress and leveraging affective computing in diagnosing and managing mood disorders more efficiently. As the narrative unfolds, the book emphasizes the crucial role of affective computing in fostering social justice and equity. It underscores the need for developing inclusive algorithms and databases while addressing ethical challenges like privacy, consent, and the risk of emotional manipulation. These discussions emphasize the significance of ethical deployment and regulation. The book also covers the technical aspects and applications of affective computing, including natural language processing for emotion recognition and analysis, voice emotion detection, and visual emotion recognition. It extends to applications, such as the use of affective computing in health management via recommender systems and personalized well-being interventions in mental health care. Addressing data challenges, "Enhancing Affective Computing with Data Augmentation: Strategies for Overcoming Limited Data Availability" presents solutions for imbalances affecting model performance. "Advancements in Multimodal Emotion Recognition" highlights the integration of facial expressions with physiological signals to improve emotion recognition accuracy and reliability. Concluding with "Ethical Considerations in Affective Computing" and "Cognitive Currents: A Path from Neuroscience to Consciousness," the book connects technical advancements in affective computing with broader ethical and philosophical inquiries surrounding consciousness and the human experience. Features: Helps readers understand the potential benefits of emotionally intelligent AI systems, such as improving mental health care, enhancing education, or promoting more ethical decision-making. Addresses ethical considerations related to the development and deployment of emotionally intelligent AI systems, helping readers to become more aware of the potential risks and trade-offs involved. Presents new approaches or frameworks for developing emotionally intelligent AI systems, providing readers with innovative ideas and perspectives. Provides examples of successful case studies where emotionally intelligent AI systems were used for social good, which may inspire readers to think about how they can contribute to society through AI development. Overall, this book will help readers gain a deeper understanding of the intersection between AI and human emotions, and how this technology can be used to create a more empathetic, compassionate, and socially responsible world.
Affective Computing
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Author : Jimmy Or
language : en
Publisher: BoD – Books on Demand
Release Date : 2008-05-01
Affective Computing written by Jimmy Or and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-05-01 with Computers categories.
This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing.
Multimodal Affective Computing Using Temporal Convolutional Neural Network And Deep Convolutional Neural Networks
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Author : Issa Ayoub
language : en
Publisher:
Release Date : 2019
Multimodal Affective Computing Using Temporal Convolutional Neural Network And Deep Convolutional Neural Networks written by Issa Ayoub and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Affective computing has gained significant attention from researchers in the last decade due to the wide variety of applications that can benefit from this technology. Often, researchers describe affect using emotional dimensions such as arousal and valence. Valence refers to the spectrum of negative to positive emotions while arousal determines the level of excitement. Describing emotions through continuous dimensions (e.g. valence and arousal) allows us to encode subtle and complex affects as opposed to discrete emotions, such as the basic six emotions: happy, anger, fear, disgust, sad and neutral. Recognizing spontaneous and subtle emotions remains a challenging problem for computers. In our work, we employ two modalities of information: video and audio. Hence, we extract visual and audio features using deep neural network models. Given that emotions are time-dependent, we apply the Temporal Convolutional Neural Network (TCN) to model the variations in emotions. Additionally, we investigate an alternative model that combines a Convolutional Neural Network (CNN) and a Recurrent Neural Network (RNN). Given our inability to fit the latter deep model into the main memory, we divide the RNN into smaller segments and propose a scheme to back-propagate gradients across all segments. We configure the hyperparameters of all models using Gaussian processes to obtain a fair comparison between the proposed models. Our results show that TCN outperforms RNN for the recognition of the arousal and valence emotional dimensions. Therefore, we propose the adoption of TCN for emotion detection problems as a baseline method for future work. Our experimental results show that TCN outperforms all RNN based models yielding a concordance correlation coefficient of 0.7895 (vs. 0.7544) on valence and 0.8207 (vs. 0.7357) on arousal on the validation dataset of SEWA dataset for emotion prediction.
Machine Learning Systems For Multimodal Affect Recognition
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Author : Markus Kächele
language : en
Publisher: Springer Nature
Release Date : 2019-11-19
Machine Learning Systems For Multimodal Affect Recognition written by Markus Kächele and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-19 with Computers categories.
Markus Kächele offers a detailed view on the different steps in the affective computing pipeline, ranging from corpus design and recording over annotation and feature extraction to post-processing, classification of individual modalities and fusion in the context of ensemble classifiers. He focuses on multimodal recognition of discrete and continuous emotional and medical states. As such, specifically the peculiarities that arise during annotation and processing of continuous signals are highlighted. Furthermore, methods are presented that allow personalization of datasets and adaptation of classifiers to new situations and persons.
Deep Learning Techniques Applied To Affective Computing
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Author : Zhen Cui
language : en
Publisher: Frontiers Media SA
Release Date : 2023-06-14
Deep Learning Techniques Applied To Affective Computing written by Zhen Cui and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-14 with Science categories.
Affective computing refers to computing that relates to, arises from, or influences emotions. The goal of affective computing is to bridge the gap between humans and machines and ultimately endow machines with emotional intelligence for improving natural human-machine interaction. In the context of human-robot interaction (HRI), it is hoped that robots can be endowed with human-like capabilities of observation, interpretation, and emotional expression. The research on affective computing has recently achieved extensive progress with many fields contributing including neuroscience, psychology, education, medicine, behavior, sociology, and computer science. Current research in affective computing concentrates on estimating human emotions through different forms of signals such as speech, face, text, EEG, fMRI, and many others. In neuroscience, the neural mechanisms of emotion are explored by combining neuroscience with the psychological study of personality, emotion, and mood. In psychology and philosophy, emotion typically includes a subjective, conscious experience characterized primarily by psychophysiological expressions, biological reactions, and mental states. The multi-disciplinary features of understanding “emotion” result in the fact that inferring the emotion of humans is definitely difficult. As a result, a multi-disciplinary approach is required to facilitate the development of affective computing. One of the challenging problems in affective computing is the affective gap, i.e., the inconsistency between the extracted feature representations and subjective emotions. To bridge the affective gap, various hand-crafted features have been widely employed to characterize subjective emotions. However, these hand-crafted features are usually low-level, and they may hence not be discriminative enough to depict subjective emotions. To address this issue, the recently-emerged deep learning (also called deep neural networks) techniques provide a possible solution. Due to the used multi-layer network structure, deep learning techniques are capable of learning high-level contributing features from a large dataset and have exhibited excellent performance in multiple application domains such as computer vision, signal processing, natural language processing, human-computer interaction, and so on. The goal of this Research Topic is to gather novel contributions on deep learning techniques applied to affective computing across the diverse fields of psychology, machine learning, neuroscience, education, behavior, sociology, and computer science to converge with those active in other research areas, such as speech emotion recognition, facial expression recognition, Electroencephalogram (EEG) based emotion estimation, human physiological signal (heart rate) estimation, affective human-robot interaction, multimodal affective computing, etc. We welcome researchers to contribute their original papers as well as review articles to provide works regarding the neural approach from computation to affective computing systems. This Research Topic aims to bring together research including, but not limited to: • Deep learning architectures and algorithms for affective computing tasks such as emotion recognition from speech, face, text, EEG, fMRI, and many others. • Explainability of deep Learning algorithms for affective computing. • Multi-task learning techniques for emotion, personality and depression detection, etc. • Novel datasets for affective computing • Applications of affective computing in robots, such as emotion-aware human-robot interaction and social robots, etc.
Multimodal Pattern Recognition Of Social Signals In Human Computer Interaction
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Author : Friedhelm Schwenker
language : en
Publisher: Springer
Release Date : 2013-03-14
Multimodal Pattern Recognition Of Social Signals In Human Computer Interaction written by Friedhelm Schwenker and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-14 with Computers categories.
This book constitutes the thoroughly refereed post-workshop proceedings of the First IAPR TC3 Workshop on Pattern Recognition of Social Signals in Human-Computer-Interaction (MPRSS2012), held in Tsukuba, Japan in November 2012, in collaboration with the NLGD Festival of Games. The 21 revised papers presented during the workshop cover topics on facial expression recognition, audiovisual emotion recognition, multimodal Information fusion architectures, learning from unlabeled and partially labeled data, learning of time series, companion technologies and robotics.