This study explored whether children’s (N=158; 4-9 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%) 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.
Child Psychology | Criminal Law | Criminal Procedure | Developmental Psychology | Evidence | Family Law | Juvenile Law | Law | Law and Psychology
Date of this Version
Kaila Bruer, Sarah Zanette, Xiaopan Ding, Thomas D. Lyon, and Kang Lee, "Identifying Liars Through Automatic Decoding of Children's Facial Expressions" (September 2019). University of Southern California Legal Studies Working Paper Series. Working Paper 306.