Determination of "Neutral"-"Pain", "Neutral"-"Pleasure", and "Pleasure"-"Pain" Affective State Distances by Using AI Image Analysis of Facial Expressions
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11240%2F22%3A10453964" target="_blank" >RIV/00216208:11240/22:10453964 - isvavai.cz</a>
Alternative codes found
RIV/00216208:11310/22:10453964
Result on the web
<a href="https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=g8xJZm5kdf" target="_blank" >https://verso.is.cuni.cz/pub/verso.fpl?fname=obd_publikace_handle&handle=g8xJZm5kdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/technologies10040075" target="_blank" >10.3390/technologies10040075</a>
Alternative languages
Result language
angličtina
Original language name
Determination of "Neutral"-"Pain", "Neutral"-"Pleasure", and "Pleasure"-"Pain" Affective State Distances by Using AI Image Analysis of Facial Expressions
Original language description
(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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20100 - Civil engineering
Result continuities
Project
<a href="/en/project/GJ19-12885Y" target="_blank" >GJ19-12885Y: Behavioral and Psycho-Physiological Response on Ambivalent Visual and Auditory Stimuli Presentation</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Technologies [online]
ISSN
2227-7080
e-ISSN
2227-7080
Volume of the periodical
10
Issue of the periodical within the volume
4
Country of publishing house
CH - SWITZERLAND
Number of pages
12
Pages from-to
75
UT code for WoS article
000845304900001
EID of the result in the Scopus database
2-s2.0-85147557847