Machine Learning Systems for Multimodal Affect Recognition

✍ Scribed by Markus Kächele

Publisher: Springer Fachmedien Wiesbaden;Springer Vieweg
Year: 2020
Tongue: English
Leaves: 198
Edition: 1st ed. 2020
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter ....Pages i-xix
Introduction (Markus Kächele)....Pages 1-6
Classification and regression approaches (Markus Kächele)....Pages 7-30
Applications and Affective corpora (Markus Kächele)....Pages 31-45
Modalities and Feature extraction (Markus Kächele)....Pages 47-62
Machine learning for the estimation of affective dimensions (Markus Kächele)....Pages 63-106
Adaptation and personalization of classifiers (Markus Kächele)....Pages 107-114
Experimental validation of pain intensity estimation (Markus Kächele)....Pages 115-130
Experimental validation of Methodological advancements (Markus Kächele)....Pages 131-135
Discussion (Markus Kächele)....Pages 137-140
Conclusion (Markus Kächele)....Pages 141-143
Back Matter ....Pages 145-187

✦ Subjects

Computer Science; User Interfaces and Human Computer Interaction; Computer Imaging, Vision, Pattern Recognition and Graphics

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