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Automatic, Dimensional and Continuous Emotion Recognition

Automatic, Dimensional and Continuous Emotion Recognition

Hatice Gunes, Maja Pantic
Copyright: © 2012 |Pages: 33
ISBN13: 9781466615953|ISBN10: 1466615958|EISBN13: 9781466615960
DOI: 10.4018/978-1-4666-1595-3.ch005
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MLA

Gunes, Hatice, and Maja Pantic. "Automatic, Dimensional and Continuous Emotion Recognition." Creating Synthetic Emotions through Technological and Robotic Advancements, edited by Jordi Vallverdú, IGI Global, 2012, pp. 73-105. https://doi.org/10.4018/978-1-4666-1595-3.ch005

APA

Gunes, H. & Pantic, M. (2012). Automatic, Dimensional and Continuous Emotion Recognition. In J. Vallverdú (Ed.), Creating Synthetic Emotions through Technological and Robotic Advancements (pp. 73-105). IGI Global. https://doi.org/10.4018/978-1-4666-1595-3.ch005

Chicago

Gunes, Hatice, and Maja Pantic. "Automatic, Dimensional and Continuous Emotion Recognition." In Creating Synthetic Emotions through Technological and Robotic Advancements, edited by Jordi Vallverdú, 73-105. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-4666-1595-3.ch005

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Abstract

Recognition and analysis of human emotions have attracted a lot of interest in the past two decades and have been researched extensively in neuroscience, psychology, cognitive sciences, and computer sciences. Most of the past research in machine analysis of human emotion has focused on recognition of prototypic expressions of six basic emotions based on data that has been posed on demand and acquired in laboratory settings. More recently, there has been a shift toward recognition of affective displays recorded in naturalistic settings as driven by real world applications. This shift in affective computing research is aimed toward subtle, continuous, and context-specific interpretations of affective displays recorded in real-world settings and toward combining multiple modalities for analysis and recognition of human emotion. Accordingly, this paper explores recent advances in dimensional and continuous affect modelling, sensing, and automatic recognition from visual, audio, tactile, and brain-wave modalities.

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