Classroom communication involves teacher’s behavior and student’s responses. Extensive research has been done on the analysis of student’s facial expressions, but the impact of instructor’s facial expressions is yet an unexplored area of research. Facial expression recognition has the potential to predict the impact of teacher’s emotions in a classroom environment. Intelligent assessment of instructor behavior during lecture delivery not only might improve the learning environment but also could save time and resources utilized in manual assessment strategies. To address the issue of manual assessment, we propose an instructor’s facial expression recognition approach within a classroom using a feedforward learning model. First, the face is detected from the acquired lecture videos and key frames are selected, discarding all the redundant frames for effective high-level feature extraction. Then, deep features are extracted using multiple convolution neural networks along with parameter tuning which are then fed to a classifier. For fast learning and good generalization of the algorithm, a regularized extreme learning machine (RELM) classifier is employed which classifies five different expressions of the instructor within the classroom. Experiments are conducted on a newly created instructor’s facial expression dataset in classroom environments plus three benchmark facial datasets, i.e., Cohn–Kanade, the Japanese Female Facial Expression (JAFFE) dataset, and the Facial Expression Recognition 2013 (FER2013) dataset. Furthermore, the proposed method is compared with state-of-the-art techniques, traditional classifiers, and convolutional neural models. Experimentation results indicate significant performance gain on parameters such as accuracy, F1-score, and recall.

中文翻译：

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利用深度特征和极限学习机进行讲师面部表情识别


课堂沟通涉及教师的行为和学生的反应。人们对学生面部表情的分析进行了广泛的研究，但教师面部表情的影响仍然是一个尚未探索的研究领域。面部表情识别有可能预测教师情绪在课堂环境中的影响。对讲师在授课期间的行为进行智能评估不仅可以改善学习环境，还可以节省手动评估策略中使用的时间和资源。为了解决手动评估的问题，我们提出了使用前馈学习模型的教师在课堂上的面部表情识别方法。首先，从获取的讲座视频中检测人脸并选择关键帧，丢弃所有冗余帧以进行有效的高级特征提取。然后，使用多个卷积神经网络以及参数调整来提取深层特征，然后将其馈送到分类器。为了快速学习和算法的良好泛化，采用正则化极限学习机（RELM）分类器，对课堂上教师的五种不同表达进行分类。实验在课堂环境中新创建的教师面部表情数据集以及三个基准面部数据集（即 Cohn–Kanade、日本女性面部表情（JAFFE）数据集和 2013 年面部表情识别（FER2013）数据集）上进行。此外，还将所提出的方法与最先进的技术、传统分类器和卷积神经模型进行了比较。 实验结果表明，准确度、 F 1 分数和召回率等参数的性能显着提升。