Background Computer vision combined with human annotation could offer a novel method for exploring facial expression (FE) dynamics in children with autism spectrum disorder (ASD). Methods We recruited 157 children with typical development (TD) and 36 children with ASD in Paris and Nice to perform two experimental tasks to produce FEs with emotional valence. FEs were explored by judging ratings and by random forest (RF) classifiers. To do so, we located a set of 49 facial landmarks in the task videos, we generated a set of geometric and appearance features and we used RF classifiers to explore how children with ASD differed from TD children when producing FEs. Results Using multivariate models including other factors known to predict FEs (age, gender, intellectual quotient, emotion subtype, cultural background), ratings from expert raters showed that children with ASD had more difficulty producing FEs than TD children. In addition, when we explored how RF classifiers performed, we found that classification tasks, except for those for sadness, were highly accurate and that RF classifiers needed more facial landmarks to achieve the best classification for children with ASD. Confusion matrices showed that when RF classifiers were tested in children with ASD, anger was often confounded with happiness. Limitations The sample size of the group of children with ASD was lower than that of the group of TD children. By using several control calculations, we tried to compensate for this limitation. Conclusion Children with ASD have more difficulty producing socially meaningful FEs. The computer vision methods we used to explore FE dynamics also highlight that the production of FEs in children with ASD carries more ambiguity.

中文翻译：

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自闭症谱系障碍儿童产生更多的模棱两可和不太有意义的社交表情：一项使用随机森林分类器的实验研究。

背景技术计算机视觉与人类注释相结合可以为探索自闭症谱系障碍（ASD）儿童的面部表情（FE）动态提供一种新颖的方法。方法我们在巴黎和尼斯招募了157名典型发育（TD）儿童和36名ASD儿童，以执行两项实验任务以产生具有情感价的FE。通过判断等级和随机森林（RF）分类器来探索FE。为此，我们在任务视频中找到了49个面部标志物，生成了一组几何特征和外观特征，并使用RF分类器探索了ASD儿童与TD儿童在制作FE时的区别。结果使用多元模型，包括已知的其他能够预测FE的因素（年龄，性别，智商，情感亚型，文化背景），专家评分者的评分显示，ASD患儿比TD患儿更难产生FE。此外，当我们探索RF分类器的工作方式时，我们发现分类任务（除针对悲伤的分类任务外）非常准确，并且RF分类器需要更多的面部标志来实现ASD儿童的最佳分类。混淆矩阵显示，当对ASD患儿进行RF分类器测试时，愤怒常常与幸福混为一谈。局限性ASD儿童组的样本量小于TD儿童组。通过使用几种控制计算，我们试图弥补这一限制。结论自闭症儿童更难产生具有社会意义的有限元。