Automatic facial expression recognition is a big challenge in human–computer interaction. Analyzing the changes in the face during a facial expression can be used for this purpose. In this paper, these changes are extracted as a number of motion vectors. These motion vectors are extracted using an optical flow algorithm. Then, they are used to analyze facial expressions by some of the data mining algorithms. This analysis has not only determined what changes occur in the face during facial expression but has also been used to recognize facial expressions. Cohen-Kanade facial expression dataset was used in this research. Based on our findings, the vertical lengths of motion vectors created in the lower part of the face have the greatest impact on the classification of facial expressions. Among the investigated classification algorithms, deep learning, support vector machine, and C5.0 had better performance, yielding an accuracy of 95.3%, 92.8%, and 90.2% respectively.

自动面部表情识别是人机交互中的一大挑战。分析面部表情期间面部的变化可以用于此目的。在本文中，这些变化被提取为多个运动向量。这些运动矢量是使用光流算法提取的。然后，它们被一些数据挖掘算法用来分析面部表情。这种分析不仅可以确定面部表情过程中面部发生的变化，还可以用于识别面部表情。本研究使用 Cohen-Kanade 面部表情数据集。根据我们的发现，面部下部创建的运动矢量的垂直长度对面部表情的分类影响最大。在所研究的分类算法中，深度学习、支持向量机和C5.0的性能较好，准确率分别为95.3%、92.8%和90.2%。