Identifying Liars Through Automatic Decoding of Children's Facial Expressions

46 Pages Posted: 6 Sep 2019 Last revised: 17 Jul 2020

See all articles by Kaila Bruer

Kaila Bruer

University of Regina

Sarah Zanette

University of Toronto

Xiaopan Ding

National University of Singapore (NUS)

Thomas D. Lyon

University of Southern California Gould School of Law

Kang Lee

Institute of Child Study

Date Written: July 16, 2020

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.

Keywords: deception detection, facial expressions, machine learning, emotions, children

Suggested Citation

Bruer, Kaila and Zanette, Sarah and Ding, Xiaopan and Lyon, Thomas D. and Lee, Kang, Identifying Liars Through Automatic Decoding of Children's Facial Expressions (July 16, 2020). 91 Child Development e995-e1011 (2020), USC CLASS Research Paper No. CLASS19-30, USC Law Legal Studies Paper No. 19-30, Available at SSRN: https://ssrn.com/abstract=3449383

Kaila Bruer (Contact Author)

University of Regina ( email )

3737 Wascana Parkway
Regina, Saskatchewan S4S OA2 S4S 0A1
Canada

Sarah Zanette

University of Toronto ( email )

Toronto, Ontario M5S 3G8
Canada

Xiaopan Ding

National University of Singapore (NUS) ( email )

1E Kent Ridge Road
NUHS Tower Block Level 7
Singapore, 119228
Singapore

Thomas D. Lyon

University of Southern California Gould School of Law ( email )

699 Exposition Boulevard
Los Angeles, CA 90089
United States
213-740-0142 (Phone)
213-740-5502 (Fax)

Kang Lee

Institute of Child Study ( email )

Toronto, Ontario M5S 3G8
Canada

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