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Machine Learning Systems for Multimodal Affect Recognition
Machine Learning Systems for Multimodal Affect Recognition
Markus Kächele
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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.
Categories:
Year:
2020
Edition:
1st ed. 2020
Publisher:
Springer Fachmedien Wiesbaden;Springer Vieweg
Language:
English
Pages:
XIX, 188
ISBN:
978-3-658-28673-6,978-3-658-28674-3
Your tags:
Computer Science; User Interfaces and Human Computer Interaction; Computer Imaging, Vision, Pattern Recognition and Graphics
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