Identifying Liars Through Automatic Decoding of Children's Facial Expressions

• Bruer, Kaila C. ^[1] ; Zanette, Sarah ^[1] ; Ding, Xiao Pan ^[2] ; Lyon, Thomas D. ^[3] ; Lee, Kang ^[1]
  1. [1] University of Toronto

     University of Toronto

     Canadá

     
  2. [2] National University of Singapore

     National University of Singapore

     Singapur

     
  3. [3] University of Southern California—Gould School of Law

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• Localización: Child development, ISSN 0009-3920, Vol. 91, Nº. 4, 2020, págs. 995-1011
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • 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.