Description

Book 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

Classification and Regression Approaches.- Applications and Affective Corpora.- Modalities and Feature Extraction.- Machine Learning for the Estimation of Affective Dimensions.- Adaptation and Personalization of Classifiers.- Experimental Validation.

Machine Learning Systems for Multimodal Affect Recognition

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    A Paperback by Markus Kächele

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      Publisher: Springer Fachmedien Wiesbaden
      Publication Date: 03/12/2019
      ISBN13: 9783658286736, 978-3658286736
      ISBN10: 3658286733
      Also in:
      Computing Human–computer interaction Image processing Machine learning

      Description

      Book 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

      Classification and Regression Approaches.- Applications and Affective Corpora.- Modalities and Feature Extraction.- Machine Learning for the Estimation of Affective Dimensions.- Adaptation and Personalization of Classifiers.- Experimental Validation.

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