Höfling T Tim A, Alpers Georg W, Büdenbender Björn, Föhl Ulrich, Gerdes Antje B M
Department of Psychology, School of Social Sciences, University of Mannheim, Mannheim, Germany.
Business School, Pforzheim University of Applied Sciences, Pforzheim, Germany.
PLoS One. 2022 Mar 3;17(3):e0263863. doi: 10.1371/journal.pone.0263863. eCollection 2022.
Automatic facial coding (AFC) is a novel research tool to automatically analyze emotional facial expressions. AFC can classify emotional expressions with high accuracy in standardized picture inventories of intensively posed and prototypical expressions. However, classification of facial expressions of untrained study participants is more error prone. This discrepancy requires a direct comparison between these two sources of facial expressions. To this end, 70 untrained participants were asked to express joy, anger, surprise, sadness, disgust, and fear in a typical laboratory setting. Recorded videos were scored with a well-established AFC software (FaceReader, Noldus Information Technology). These were compared with AFC measures of standardized pictures from 70 trained actors (i.e., standardized inventories). We report the probability estimates of specific emotion categories and, in addition, Action Unit (AU) profiles for each emotion. Based on this, we used a novel machine learning approach to determine the relevant AUs for each emotion, separately for both datasets. First, misclassification was more frequent for some emotions of untrained participants. Second, AU intensities were generally lower in pictures of untrained participants compared to standardized pictures for all emotions. Third, although profiles of relevant AU overlapped substantially across the two data sets, there were also substantial differences in their AU profiles. This research provides evidence that the application of AFC is not limited to standardized facial expression inventories but can also be used to code facial expressions of untrained participants in a typical laboratory setting.
自动面部编码（AFC）是一种用于自动分析面部情绪表达的新型研究工具。在经过精心设计的、具有代表性的标准化图片库中，AFC能够高精度地对面部情绪表达进行分类。然而，对未经训练的研究参与者的面部表情进行分类时更容易出错。这种差异需要对这两种面部表情来源进行直接比较。为此，我们邀请了70名未经训练的参与者，在典型的实验室环境中表达喜悦、愤怒、惊讶、悲伤、厌恶和恐惧。录制的视频使用一款成熟的AFC软件（FaceReader，Noldus信息技术公司）进行评分。将这些评分与70名训练有素的演员的标准化图片的AFC测量结果（即标准化图片库）进行比较。我们报告了特定情绪类别的概率估计值，此外，还报告了每种情绪的动作单元（AU）特征。基于此，我们使用了一种新颖的机器学习方法，分别针对两个数据集确定每种情绪的相关动作单元。首先，未经训练的参与者的某些情绪的误分类更为频繁。其次，对于所有情绪，未经训练的参与者的图片中的动作单元强度通常低于标准化图片。第三，尽管两个数据集的相关动作单元特征有很大重叠，但它们的动作单元特征也存在显著差异。这项研究提供了证据，表明AFC的应用不仅限于标准化面部表情图片库，还可用于对典型实验室环境中未经训练的参与者的面部表情进行编码。
