Identifying Liars Through Automatic Decoding of Children's Facial Expressions.
Identifying Liars Through Automatic Decoding of Children's Facial Expressions.
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DOI：
10.1111/cdev.13336
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发表时间：
2020-07
影响因子：
4.6
通讯作者：
Lee K
中科院分区：
文献类型：
--
作者：
Bruer KC;Zanette S;Ding XP;Lyon TD;Lee K
本研究探讨了儿童（N = 158; 4- 9岁）的非语言面部表情是否可以用来识别儿童是否在欺骗。使用计算机视觉程序根据面部动作编码系统自动解码儿童的面部表情，这项研究采用机器学习来确定面部表情是否可以用来区分隐藏破坏玩具的儿童（说谎者）和没有破坏玩具的儿童（非说谎者）。结果发现，无论年龄或虐待史，儿童的面部表情可以准确地（73%）区分说谎者和非说谎者。说谎者比非说谎者更强烈地表达两种情绪：惊讶和恐惧。这些发现为使用自动编码的面部表情来检测儿童的欺骗行为提供了证据。
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.
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影响因子：
4
作者：
Evans, Angela D.;Xu, Fen;Lee, Kang
通讯作者：
Lee, Kang
影响因子：
2
作者：
Goodman, GS;Quas, JA;Shapiro, C
通讯作者：
Shapiro, C
影响因子：
4.8
作者：
CONTE, JR;WOLF, S;SMITH, T
通讯作者：
SMITH, T
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y
影响因子：
3.7
作者：
Flavell, JH
通讯作者：
Flavell, JH