Identifying Liars Through Automatic Decoding of Children's Facial Expressions.

Autor: Bruer KC; University of Toronto.; University of Regina., Zanette S; University of Toronto., Ding XP; National University of Singapore., Lyon TD; University of Southern California-Gould School of Law., Lee K; University of Toronto.
Jazyk: angličtina
Zdroj: Child development [Child Dev] 2020 Jul; Vol. 91 (4), pp. e995-e1011. Date of Electronic Publication: 2019 Nov 04.
DOI: 10.1111/cdev.13336
Abstrakt: This study explored whether children's (N = 158; 4- to 9 years old) nonverbal facial expressions can be used to identify when children are being deceptive. Using a computer vision program to automatically decode children's facial expressions according to the Facial Action Coding System, this study employed machine learning to determine whether facial expressions can be used to discriminate between children who concealed breaking a toy(liars) and those who did not break a toy(nonliars). Results found that, regardless of age or history of maltreatment, children's facial expressions could accurately (73%) be distinguished between liars and nonliars. Two emotions, surprise and fear, were more strongly expressed by liars than nonliars. These findings provide evidence to support the use of automatically coded facial expressions to detect children's deception.
(© 2019 Society for Research in Child Development.)
Databáze: MEDLINE
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