支持MOOC课程的动态表情识别算法

Dynamic Facial Expression Recognition Algorithm for Massive Open Online Courses

大规模在线开放课程(MOOC)的讲授主要依赖于视频录像,讲者无法直接获取学生的听课状态.而在线下的面授课程中,讲者可以通过学生面部的表情,及时获得教学效果的反馈,并改进教学过程.针对此问题,提出将学生学习过程中的面部表情,引入到MOOC课程,并定义了7种听课表情,设计了动态表情识别算法.首先按照5帧的间隔从学生的摄像头视频中抽取面面部图像,并利用FACE++提取面部特征点.其次,通过支持向量机(SVM)识别与表情相关面部关键部位的特征模式,作为分类树的结点.最后利用各个关键部位的特征进行组合识别面部表情.实验表明,该算法可以不拘泥于传统算法仅仅支持6种基本表情识别,在MOOC应用上有更好的效果.

The massive online open course （MOOC） is mainly dependent on video,the lecturer can not directly access to the student′s state of listening.However in the face-to-face course,the lecturer can timely access to the feedback,and improve the teaching process through the student′s facial expression.In order to solve this problem,the student′sfacial expression identification was introducedinto MOOC,7 kinds of expressions are defined,and a dynamic expression recognition algorithm is explored.Firstly,the face images are extracted from the video captured by camera at 5 frames intervals,and then its are processed by FACE＋＋ to select facial feature points.Secondly,the selected several feature modes of the important parts of face studied by the SVM are convert to the nodes of the classification tree.Finally,the facial expressions are recognized by the combination of feature modes of the important face parts.Experiment shows that the algorithm does not be limited to recognize the 6 kinds of traditional facial expression recognition,it has better effect on the MOOC.