Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning
Facial emotion recognition with transition detection for students with high-functioning autism in adaptive e-learning
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DOI：
10.1007/s00500-017-2549-z
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发表时间：
2018-05-01
期刊：
影响因子：
4.1
通讯作者：
Chen, Yuh-Min
中科院分区：
文献类型：
--
作者：
Chu, Hui-Chuan;Tsai, William Wei-Jen;Chen, Yuh-Min
情绪对学习成绩有很大的影响。在高功能自闭症（HFA）学生的情况下，焦虑和愤怒等负面情绪会损害学习过程，因为这些人无法控制自己的情绪。一旦HFA学生的负面情绪发生，试图对其进行调节，随后对HFA学生的调节往往是无效的，因为很难让他们平静下来。因此，在电子学习环境中，检测情绪转变并及时提供自适应情绪调节策略来调节负面情绪对HFA学生来说尤为重要。提出了一种基于表情转换检测的情感识别方法。为了建立情感识别的分类器，进行了一个情感诱导实验，以收集基于面部的标志信号。该方法采用滑动窗口技术和支持向量机（SVM）建立分类器，以识别情感。为了确定用于情感识别的鲁棒特征，信息增益（IG）和卡方用于特征评估。还研究了不同滑动窗口参数的分类器的有效性。实验结果表明，该方法具有足够的判别能力。对基本情绪和过渡情绪的识别率分别为99.13%和92.40%。此外，通过特征选择，训练时间加快了4.45倍，基本情绪和过渡情绪的识别率分别为97.97%和87.49%。将该方法应用于一个数学自适应网络学习环境中，验证了其应用效果。
Emotions deeply affect learning achievement. In the case of students with high-functioning autism (HFA), negative emotions such as anxiety and anger can impair the learning process due to the inability of these individuals to control their emotions. Attempts to regulate negative emotions in HFA students once they have occurred, subsequent regulation to HFA students is often ineffective because it is difficult to calm them down. Hence, detecting emotional transitions and providing adaptive emotional regulation strategies in a timely manner to regulate negative emotions can be especially important for students with HFA in an e-learning environment. In this study, a facial expression-based emotion recognition method with transition detection was proposed. An emotion elicitation experiment was performed to collect facial-based landmark signals for the purpose of building classifiers of emotion recognition. The proposed method used sliding window technique and support vector machine (SVM) to build classifiers in order to recognize emotions. For the purpose of determining robust features for emotion recognition, Information Gain (IG) and Chi-square were used for feature evaluations. The effectiveness of classifiers with different parameters of sliding windows was also examined. The experimental results confirmed that the proposed method has sufficient discriminatory capability. The recognition rates for basic emotions and transitional emotions were 99.13 and 92.40%, respectively. Also, through feature selection, training time was accelerated by 4.45 times, and the recognition rates for basic emotions and transitional emotions were 97.97 and 87.49%, respectively. The method was applied in an adaptive e-learning environment for mathematics to demonstrate its application effectiveness.