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Continuous Facial Emotion Recognition Method Based on Deep Learning of Academic Emotions

机译：基于学术情绪深度学习的连续面部情感识别方法

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摘要

It is important to comprehend students' academic emotions in interactive teaching environments. Academic emotions refer to facial expressions that students display along with their academic performance in a learning process. By noting students' academic emotions, teachers can provide the most suitable teaching material according to the emotions to improve their academic performance and motivation. The results can also be subsequently applied to adaptive learning. Recently, some researchers have attempted to study academic emotions with the aid of facial and emotion recognition technologies. However, most studies focused on the analysis and recognition of a single image. It was not considered that academic emotions are a continuous expression in response to the learning situation over a period of time. To address this problem, a continuous facial emotional pattern recognition method based on deep learning is proposed in this study to analyze academic emotions. This method combines the convolutional neural network (CNN) and the long short-term memory (LSTM) network for deep learning to recognize and analyze the continuous facial academic emotional pattern of students and thus recognize academic emotions. Through this method, the e-learning system can understand the learning progress of students quickly and accurately, and offer the students appropriate teaching materials to enhance their academic performance and motivation. The experimental results showed that the recognition accuracies of the CNN model and CNN plus LSTM were 72.47 and 84.33%, respectively. The combination of two neural networks improved the accuracy by approximately 12% compared with that for the CNN alone.
机译：在互动教学环境中理解学生的学术情绪非常重要。学术情绪是指学生在学习过程中表现出的面部表情。通过注意学生的学术情绪，教师可以根据情绪提供最合适的教材，以提高他们的学业成绩和动机。结果也可以随后应用于自适应学习。最近，一些研究人员试图借助面部和情感识别技术研究学术情绪。然而，大多数研究专注于分析和识别单个图像。并不认为学术情绪是在一段时间内响应学习情况的持续表达。为了解决这个问题，在本研究中提出了一种基于深度学习的连续面部情感模式识别方法，分析学术情绪。该方法结合了卷积神经网络（CNN）和长短期记忆（LSTM）网络，以便深入学习识别和分析学生的连续面部学术情感模式，从而承认学术情绪。通过这种方法，电子学习系统可以快速准确地了解学生的学习进度，并为学生提供适当的教学材料，以提高他们的学业成绩和动机。实验结果表明，CNN模型和CNN加LSTM的识别精度分别为72.47和84.33％。与单独的CNN相比，两个神经网络的组合将精度提高了大约12％。

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  • 来源
    《Sensors and materials》 |2020年第10期|3243-3259|共17页
  • 作者

    Szu-Yin Lin; Chao-Ming Wu; Shih-Lun Chen; Ting-Lan Lin; Yi-Wen Tseng;

  • 作者单位

    Department of Computer Science and Information Engineering National Ilan University No. 1 Section 1 Shennong Road Yilan City Yilan County 26047 Taiwan;

    Department of Information Management Chung Yuan Christian University No. 200 Chung Pei Road Chung Li District Taoyuan City 32023 Taiwan;

    Department of Electronic Engineering Chung Yuan Christian University No. 200 Chung Pei Road Chung Li District Taoyuan City 32023 Taiwan;

    Department of Electronic Engineering National Taipei University of Technology No. 1 Section 3 Zhongxiao East Road Taipei City 10608 Taiwan;

    Department of Information Management Chung Yuan Christian University No. 200 Chung Pei Road Chung Li District Taoyuan City 32023 Taiwan;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    academic emotions; face emotion recognition; deep learning; convolutional neural networks; long short-term memory networks;

    机译：学术情绪;面对情感识别;深度学习;卷积神经网络;长短期内存网络;

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