Friday, September 13, 2019
Kaila Bruer, Sarah Zanette, Xiaopan Ding, Thomas D. Lyon and Kang Lee (University of Regina, University of Toronto, National University of Singapore (NUS), University of Southern California Gould School of Law and Institute of Child Study) have posted Identifying Liars Through Automatic Decoding of Children's Facial Expressions (Forthcoming in Child Development) on SSRN. Here is the abstract:
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.