Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems
Published on 14. November 2021
Book
Paperback/Softback
XI, 247 pages
978-3-030-61579-6 (ISBN)
Description
This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the development needs of emotional robots and emotional human-robot interaction (HRI) systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.
More details
Series
Edition
2021 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
45 s/w Abbildungen, 85 farbige Abbildungen
XI, 247 p. 130 illus., 85 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 15 mm
Weight
406 gr
ISBN-13
978-3-030-61579-6 (9783030615796)
DOI
10.1007/978-3-030-61577-2
Schweitzer Classification
Other editions
Additional editions
Luefeng Chen | Min Wu | Witold Pedrycz
Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems
Book
11/2020
Springer
€181.89
Shipment within 7-9 days
Content
Introduction.- Multi-modal emotion feature extraction.- Deep sparse autoencoder network for facial emotion recognition.- AdaBoost-knn with direct optimization for dynamic emotion recognition.- Weight-adapted convolution neural network for facial expression recognition.- Two-layer fuzzy multiple random forest for speech emotion recognition.- Two-stage fuzzy fusion based-convolution neural network for dynamic emotion recognition.- Multi-support vector machine based Dempster-Shafer theory for gesture intention understanding.- Three-layer weighted fuzzy support vector regressions for emotional intention understanding.- Dynamic emotion understanding based on two-layer fuzzy fuzzy support vector regression-Takagi-Sugeno model.- Emotion-age-gender-nationality based intention understanding using two-layer fuzzy support vector regression.- Emotional human-robot interaction systems.- Experiments and applications of emotional human-robot.