Determination of "Neutral"-"Pain", "Neutral"-"Pleasure", and "Pleasure"-"Pain" Affective State Distances by Using AI Image Analysis of Facial Expressions

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/00216208:11310/22:10453964

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Determination of "Neutral"-"Pain", "Neutral"-"Pleasure", and "Pleasure"-"Pain" Affective State Distances by Using AI Image Analysis of Facial Expressions

Popis výsledku v původním jazyce

(1) Background: In addition to verbalizations, facial expressions advertise one&apos;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.

Název v anglickém jazyce

Determination of "Neutral"-"Pain", "Neutral"-"Pleasure", and "Pleasure"-"Pain" Affective State Distances by Using AI Image Analysis of Facial Expressions

Popis výsledku anglicky

(1) Background: In addition to verbalizations, facial expressions advertise one&apos;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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20100 - Civil engineering

Projekt

<a href="/cs/project/GJ19-12885Y" target="_blank" >GJ19-12885Y: Behaviorální a psycho-fyziologická reakce na prezentaci ambivalentních obrazových a zvukových stimulů</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2022

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Technologies [online]

ISSN

2227-7080

e-ISSN

2227-7080

Svazek periodika

10

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

CH - Švýcarská konfederace

Počet stran výsledku

12

Strana od-do

75

Kód UT WoS článku

000845304900001

EID výsledku v databázi Scopus

2-s2.0-85147557847