Titlebook: Machine Learning Systems for Multimodal Affect Recognition; Markus Kächele Book 2020 Springer Fachmedien Wiesbaden GmbH, part of Springer

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Experimental validation of pain intensity estimation, setting allows experiments of the “binary kind”, i.e. pain vs. no pain, but also those with a more fine-grained distinction of the individual pain levels. Then, the results of the personalization using the proposed similarity measures are presented, followed by the results of the adaptation methodology.

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Conclusion,te and continuous recognition of emotional states using multi-modal input streams. This work touches and contributes to each aspect of the processing pipeline from recording over pre-processing and feature computation to post-processing and classification steps.

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Applications and Affective corpora,In this chapter, a brief overview of affective categories is given before the utilized data collections are introduced. Finally, a few points regarding corpus design and the annotation process are discussed.

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Machine learning for the estimation of affective dimensions,The recognition of affective states can be further divided into recognition of fixed categories such as . or . (compare Section 3.1) and continuous estimations according to affective dimensions such as . or ..

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