Identifying Liars Through Automatic Decoding of Children's Facial Expressions. Issue 4 (4th November 2019)

• Record Type: Journal Article
• Authors: Bruer, Kaila C.; Zanette, Sarah; Ding, Xiao Pan; Lyon, Thomas D.; Lee, Kang
• Is Part Of: Child development. Volume 91:Issue 4(2020)
• Page Start: e995
• Page End: e1011
• Publication Date: 2019-11-04
• Subjects: Child development -- Periodicals; Child psychology -- Periodicals; Child Development -- Periodicals; Child Development; Pediatrics -- Periodicals; Pedicatrics; 305.231
• DOI: 10.1111/cdev.13336
• Languages: English
• ISSNs: 0009-3920

Abstract

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.