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Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives

Griffin, HJ; Aung, MSH; Romera-Paredes, B; McLoughlin, C; McKeown, G; Curran, W; Bianchi-Berthouze, N; (2015) Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives. IEEE Transactions on Affective Computing , 6 (2) pp. 165-178. 10.1109/TAFFC.2015.2390627. Green open access

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Abstract

Despite its importance in social interactions, laughter remains little studied in affective computing. Intelligent virtual agents are often blind to users’ laughter and unable to produce convincing laughter themselves. Respiratory, auditory, and facial laughter signals have been investigated but laughter-related body movements have received less attention. The aim of this study is threefold. First, to probe human laughter perception by analyzing patterns of categorisations of natural laughter animated on a minimal avatar. Results reveal that a low dimensional space can describe perception of laughter “types”. Second, to investigate observers’ perception of laughter (hilarious, social, awkward, fake, and non-laughter) based on animated avatars generated from natural and acted motion-capture data. Significant differences in torso and limb movements are found between animations perceived as laughter and those perceived as non-laughter. Hilarious laughter also differs from social laughter. Different body movement features were indicative of laughter in sitting and standing avatar postures. Third, to investigate automatic recognition of laughter to the same level of certainty as observers’ perceptions. Results show recognition rates of the Random Forest model approach human rating levels. Classification comparisons and feature importance analyses indicate an improvement in recognition of social laughter when localized features and nonlinear models are used.

Type: Article
Title: Perception and Automatic Recognition of Laughter from Whole-Body Motion: Continuous and Categorical Perspectives
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TAFFC.2015.2390627
Publisher version: https://doi.org/10.1109/TAFFC.2015.2390627
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Observers, Animation, Avatars, Face recognition, Face, Videos, Acoustics
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
URI: https://discovery.ucl.ac.uk/id/eprint/10074443
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