Mapping the emotional face. How individual face parts contribute to successful emotion recognition.
Mapping the emotional face. How individual face parts contribute to successful emotion recognition.
复制标题
DOI：
10.1371/journal.pone.0177239
复制
发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Kissler J
中科院分区：
文献类型：
--
作者：
Wegrzyn M;Vogt M;Kireclioglu B;Schneider J;Kissler J
哪些面部特征可以让人类观察者成功地识别出情绪的表达？虽然眼睛和嘴巴经常被证明是非常重要的，但对面部动作单位的研究对表达每种情绪所涉及的区域做出了更精确的预测。目前的研究是在细粒度层面上进行的，即在解码面部表情时最依赖的物理特征。在实验中，根据埃克曼的说法，表达基本情绪的个体脸被隐藏在一个由48块瓷砖组成的面具后面，面具被依次揭开。参与者被要求在识别出面部表情后立即停止这个序列，并给它分配正确的标签。对于面部的每个部分，计算其对成功识别的贡献，允许可视化不同面部区域对每个表情的重要性。总的来说，观察者在成功识别情绪时主要依靠眼睛和嘴巴区域。此外，眼睛和嘴的重要性不同，可以将表情组合在一个连续的空间中，从悲伤和恐惧（依靠眼睛）到厌恶和快乐（嘴巴）。对表情识别具有最高诊断价值的面部部位通常位于面部动作编码系统的动作单元对应的区域。一项对面部不同部位对表情识别有用性的相似性分析表明，面部的聚类是根据它们所表达的情感，而不是低水平的身体特征。此外，在构建的相似空间中，更多依赖于眼睛或嘴巴区域的表情更接近。这些分析通过描绘从面部特征到心理表征的映射，有助于更好地理解人类观察者如何处理情感表达。
Which facial features allow human observers to successfully recognize expressions of emotion? While the eyes and mouth have been frequently shown to be of high importance, research on facial action units has made more precise predictions about the areas involved in displaying each emotion. The present research investigated on a fine-grained level, which physical features are most relied on when decoding facial expressions. In the experiment, individual faces expressing the basic emotions according to Ekman were hidden behind a mask of 48 tiles, which was sequentially uncovered. Participants were instructed to stop the sequence as soon as they recognized the facial expression and assign it the correct label. For each part of the face, its contribution to successful recognition was computed, allowing to visualize the importance of different face areas for each expression. Overall, observers were mostly relying on the eye and mouth regions when successfully recognizing an emotion. Furthermore, the difference in the importance of eyes and mouth allowed to group the expressions in a continuous space, ranging from sadness and fear (reliance on the eyes) to disgust and happiness (mouth). The face parts with highest diagnostic value for expression identification were typically located in areas corresponding to action units from the facial action coding system. A similarity analysis of the usefulness of different face parts for expression recognition demonstrated that faces cluster according to the emotion they express, rather than by low-level physical features. Also, expressions relying more on the eyes or mouth region were in close proximity in the constructed similarity space. These analyses help to better understand how human observers process expressions of emotion, by delineating the mapping from facial features to psychological representation.