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Measures and metrics for automatic emotion classification via FACET

Dente, P; Kuester, D; Skora, L; Krumhuber, E; (2017) Measures and metrics for automatic emotion classification via FACET. In: Bryson, J and De Vos, M and Padget, J, (eds.) (Proceedings) AISB Annual Convention 2017. (pp. pp. 160-163). : Bath, UK. (In press). Green open access

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Abstract

For dynamic emotions to be modelled in a natural and convincing way, systems must rely on accurate affective analysis of facial expressions in the first place. The present work introduces two measures for evaluating automatic emotion classification performance. It further provides a systematic comparison between 14 databases of dynamic expressions. Machine analysis was conducted using the FACET system, with an algorithm calculating recognition sensitivity and confidence. Results revealed the proportion of facial stimuli that could be recognised by the machine algorithm above threshold evidence, showing significant differences in recognition performance between the databases.

Type: Proceedings paper
Title: Measures and metrics for automatic emotion classification via FACET
Event: AISB Annual Convention 2017
Location: Bath, UK
Dates: 18 April 2017-21 April 2017
Open access status: An open access version is available from UCL Discovery
Publisher version: http://aisb2017.cs.bath.ac.uk/draft-proceedings.pd...
Language: English
UCL classification: UCL > Provost and Vice Provost Offices
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 > Experimental Psychology
URI: https://discovery.ucl.ac.uk/id/eprint/1546813
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