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Acted emotional expressions of game-playing children: investigating the influence of emotion intensity on recognition rates

Published: 27 April 2013 Publication History

Abstract

While the intensity of emotions is likely of great importance to automatic emotion recognition systems, it is not an ordinary feature in emotion databases. This paper presents a database of children acting out six basic emotions, in which the intensity of said emotions was manipulated. A judgment task showed emotions were better recognized than chance could predict, while differences in intensity were perceived in two manipulated conditions. In addition, we establish a correlation between intensity and recognition rates for this database. Finally, possibilities for future research are discussed.

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Cited By

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  • (2024)Emotion Detection from Real-Life Situations Based on Journal Entries Using Machine Learning and Deep Learning TechniquesIntelligent Systems and Applications10.1007/978-3-031-47724-9_32(477-502)Online publication date: 19-Apr-2024
  • (2018)HaruProceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3171221.3171288(233-240)Online publication date: 26-Feb-2018

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  1. Acted emotional expressions of game-playing children: investigating the influence of emotion intensity on recognition rates

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    cover image ACM Conferences
    CHI EA '13: CHI '13 Extended Abstracts on Human Factors in Computing Systems
    April 2013
    3360 pages
    ISBN:9781450319522
    DOI:10.1145/2468356
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 27 April 2013

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    Author Tags

    1. acted emotions
    2. automatic recognition
    3. children
    4. emotional expressions
    5. perception test

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    CHI EA '13 Paper Acceptance Rate 630 of 1,963 submissions, 32%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    • (2024)Emotion Detection from Real-Life Situations Based on Journal Entries Using Machine Learning and Deep Learning TechniquesIntelligent Systems and Applications10.1007/978-3-031-47724-9_32(477-502)Online publication date: 19-Apr-2024
    • (2018)HaruProceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction10.1145/3171221.3171288(233-240)Online publication date: 26-Feb-2018

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