Machine Learning Systems for Multimodal Affect Recognition

Discover the intricate world of affective computing with Machine Learning Systems for Multimodal Affect Recognition by Markus Kächele. Published by Springer Fachmedien Wiesbaden in 2019, this insightful paperback spans 188 pages and provides a comprehensive exploration of the various stages involved in the affective computing pipeline. From corpus design and recording to annotation, feature extraction, and classification of individual modalities, Kächele meticulously covers each step. The book also delves into post-processing techniques and the fusion of data in ensemble classifiers, making it an essential resource for researchers and practitioners in the field. Enhance your understanding of multimodal affect recognition and its applications with this authoritative guide.