Stöckli, Sabrina; Schulte-Mecklenbeck, Michael; Borer, Stefan; Samson, Andrea C. (2018). Facial expression analysis with AFFDEX and FACET: A validation study. Behavior research methods, 50(4), pp. 1446-1460. Psychonomic Society 10.3758/s13428-017-0996-1
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Stöckli Schulte-Mecklenbeck Borer Samson 2017.pdf - Published Version Available under License Publisher holds Copyright. Download (627kB) | Preview |
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Stöckli et al. 2018.pdf - Accepted Version Available under License Publisher holds Copyright. Download (261kB) | Preview |
The goal of this study was to validate AFFDEX and FACET, two algorithms classifying emotions from facial expressions, in iMotions’s software suite. In Study 1, pictures of standardized emotional facial expressions from three databases, the Warsaw Set of Emotional Facial Expression Pictures (WSEFEP), the Amsterdam Dynamic Facial Expression Set (ADFES), and the Radboud Faces Database (RaFD), were classified with both modules. Accuracy (Matching Scores) was computed to assess and compare the classification quality. Results show a large variance in accuracy across emotions and databases, with a performance advantage for FACET over AFFDEX. In Study 2, 110 participants’ facial expressions were measured while being exposed to emotionally evocative pictures from the International Affective Picture System (IAPS), the Geneva Affective Picture Database (GAPED) and the Radboud Faces Database (RaFD). Accuracy again differed for distinct emotions, and FACET performed better. Overall, iMotions can achieve acceptable accuracy for standardized pictures of prototypical (vs. natural) facial expressions, but performs worse for more natural facial expressions. We discuss potential sources for limited validity and suggest research directions in the broader context of emotion research.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
03 Faculty of Business, Economics and Social Sciences > Department of Business Management > Institute of Innovation Management > Consumer Behavior |
UniBE Contributor: |
Stöckli, Sabrina (A), Schulte-Mecklenbeck, Michael, Borer, Stefan |
Subjects: |
600 Technology > 650 Management & public relations 600 Technology > 610 Medicine & health |
ISSN: |
1554-3528 |
Publisher: |
Psychonomic Society |
Language: |
English |
Submitter: |
Daniela Lüdi |
Date Deposited: |
04 Jul 2018 09:05 |
Last Modified: |
29 Mar 2023 23:36 |
Publisher DOI: |
10.3758/s13428-017-0996-1 |
PubMed ID: |
29218587 |
BORIS DOI: |
10.7892/boris.117417 |
URI: |
https://boris.unibe.ch/id/eprint/117417 |
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