Editor: Gyanendra K. Verma

Multimodal Affective Computing: Affective Information Representation, Modelling, and Analysis

eBook: US $49 Special Offer (PDF + Printed Copy): US $79
Printed Copy: US $55
Library License: US $196
ISBN: 978-981-5124-46-0 (Print)
ISBN: 978-981-5124-45-3 (Online)
Year of Publication: 2023
DOI: 10.2174/97898151244531230101
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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.


Affective Computing is a new area aiming to create intelligent computers that recognize, understand, and process human emotions. Affective Computing is an interdisciplinary area that encompasses a variety of disciplines, such as computer science, psychology, and cognitive science, among others. Emotion may be communicated in various ways, including gestures, postures, and facial expressions, as well as physiological signs, including brain activity, heart rate, muscle activity, blood pressure, and skin temperature. Humans can perceive emotion through facial expressions in general. However, not all emotions, particularly complex ones such as pride, love, mellowness, and sorrow, can be identified only through facial expressions. Physiological signals can therefore be utilized to represent complex emotions effectively.

This book aims to provide the audience with a basic understanding of Affective Computing and its application in many research fields. This state-of-the-art review of existing emotion theory and modeling approaches will help the readers explore various aspects of Affective Computing. By the end of the book, I hope that the readers will be able to understand emotion recognition methods based on audio, video, and physiological signals. Moreover, they will learn the fusion framework and familiarity to implement for emotion recognition.

Dr. Shitala Prasad
Scientist, Institute for Infocomm Research