MDPI Books
Go to cart
Reprint
Download Flyer

Ubiquitous Technologies for Emotion Recognition

Edited by
August 2021
194 pages
  • ISBN978-3-0365-1802-2 (Hardback)
  • ISBN978-3-0365-1801-5 (PDF)
https://doi.org/10.3390/books978-3-0365-1801-5
Free download (PDF)

This book is a reprint of the Special Issue Ubiquitous Technologies for Emotion Recognition that was published in

Biology & Life Sciences
Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now, with the advent of wearable, mobile, and ubiquitous technologies, that we can aim to sense and recognize emotions, continuously and in real time. This book brings together the latest experiences, findings, and developments regarding ubiquitous sensing, modeling, and the recognition of human emotions.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
self-management interview application; emotion analysis; facial recognition; image-mining; deep convolutional neural network; emotion recognition; pattern recognition; texture descriptors; mobile tool; neuromarketing; brain computer interface (BCI); consumer preferences; EEG signal; deep learning; deep neural network (DNN); emotion recognition; electroencephalogram (EEG); logistic regression; Gaussian kernel; Laplacian prior; affective computing; human–robot interaction; thermal IR imaging; affective computing; social robots; emotion recognition; facial expression analysis; line segment feature analysis; dimensionality reduction; convolutional recurrent neural network; driver health risk; emotion recognition; intelligent speech signal processing; affective computing; human computer interaction; supervised learning; computer vision; deep learning; optical flow; micro facial expressions; real-time processing; driver stress state; IR imaging; machine learning; support vector machine (SVR); advanced driver-assistance systems (ADAS); affective computing; emotion recognition; artificial intelligence; machine learning; image processing; video processing