Abstract
Affective computing has been an extremely active research and development area for some years now, with some of the early results already starting to be integrated in human-computer interaction systems. Driven mainly by research initiatives in Europe, USA and Japan and accelerated by the abundance of processing power and low-cost, unintrusive sensors like cameras and microphones, affective computing functions in an interdisciplinary fashion, sharing concepts from diverse fields, such as signal processing and computer vision, psychology and behavioral sciences, human-computer interaction and design, machine learning, and so on. In order to form relations between low-level input signals and features to high-level concepts such as emotions or moods, one needs to take into account the multitude of psychology and representation theories and research findings related to them and deploy machine learning techniques to actually form computational models of those. This chapter elaborates on the concepts related to affective computing, how these can be connected to measurable features via representation models and how they can be integrated into human-centric applications.
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Malatesta, L., Karpouzis, K., Raouzaiou, A. (2009). Affective Intelligence: The Human Face of AI. In: Bramer, M. (eds) Artificial Intelligence An International Perspective. Lecture Notes in Computer Science(), vol 5640. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03226-4_4
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