Title
Determination of “Neutral”–“Pain”, “Neutral”–“Pleasure”, and “Pleasure”–“Pain” Affective State Distances by Using AI Image Analysis of Facial Expressions
Author
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Abstract
(1) Background: In addition to verbalizations, facial expressions advertise one’s affective state. There is an ongoing debate concerning the communicative value of the facial expressions of pain and of pleasure, and to what extent humans can distinguish between these. We introduce a novel method of analysis by replacing human ratings with outputs from image analysis software. (2) Methods: We use image analysis software to extract feature vectors of the facial expressions neutral, pain, and pleasure displayed by 20 actresses. We dimension-reduced these feature vectors, used singular value decomposition to eliminate noise, and then used hierarchical agglomerative clustering to detect patterns. (3) Results: The vector norms for pain–pleasure were rarely less than the distances pain–neutral and pleasure–neutral. The pain–pleasure distances were Weibull-distributed and noise contributed 10% to the signal. The noise-free distances clustered in four clusters and two isolates. (4) Conclusions: AI methods of image recognition are superior to human abilities in distinguishing between facial expressions of pain and pleasure. Statistical methods and hierarchical clustering offer possible explanations as to why humans fail. The reliability of commercial software, which attempts to identify facial expressions of affective states, can be improved by using the results of our analyses.
Keywords
image processingartificial intelligencefacial expressionsaffective state expressionfacial pain expressionfacial pleasure expressionBDSM videoshierarchical agglomerative clusteringautoencoder neural network
Object type
Language
English [eng]
Persistent identifier
phaidra.univie.ac.at/o:1593994
Appeared in
Title
Technologies
Volume
10
Issue
4
ISSN
2227-7080
Issued
2022
Publication
MDPI AG
Version type
Date issued
2022
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Rights
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© 2022 by the authors
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