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- Title
Identifying Liars Through Automatic Decoding of Children's Facial Expressions.
- Authors
Bruer, Kaila C.; Zanette, Sarah; Ding, Xiao Pan; Lyon, Thomas D.; Lee, Kang
- Abstract
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.
- Subjects
TRUTHFULNESS &; falsehood in children; FACIAL expression; NONVERBAL communication in children; MACHINE learning; COMPUTER vision; LIE detectors &; detection; RESEARCH; RESEARCH methodology; MEDICAL cooperation; EVALUATION research; COMPARATIVE studies; INFORMATION science; RESEARCH funding; DECEPTION
- Publication
Child Development, 2020, Vol 91, Issue 4, pe995
- ISSN
0009-3920
- Publication type
journal article
- DOI
10.1111/cdev.13336