EXPRESSION RECOGNITION SYSTEM BASED ON CONVOLUTIONAL NEURAL NETWORK

Authors

  • Huihui Wang Xuzhou University of Technology, Xuzhou, Jiangsu, China.
  • Dan Li (Corresponding Author) Xuzhou University of Technology, Xuzhou, Jiangsu, China.
  • Ruiqun Xu Xuzhou University of Technology, Xuzhou, Jiangsu, China.
  • Hengjia Zhang Xuzhou University of Technology, Xuzhou, Jiangsu, China.
  • Yi Liu Xuzhou University of Technology, Xuzhou, Jiangsu, China.
  • Bohua Li Xuzhou University of Technology, Xuzhou, Jiangsu, China.

Keywords:

Artificial intelligence, Deep convolutional neural network, Expression recognition, Classroom status recognition

Abstract

With the continuous development of artificial intelligence technology, student expression recognition in the classroom has become an important research direction in the field of education. However, existing expression recognition methods often have problems such as low classification accuracy and high recognition difficulty, making it difficult to meet the needs of practical applications. In order to solve these problems, this paper proposes a method for student classroom expression recognition based on convolutional neural network. By collecting images of students' classroom expressions and using technologies such as preprocessing, feature extraction, and model training, we can accurately identify students' classroom expressions, monitor students' status in real time, and remind teachers to change the classroom atmosphere to help students adjust in time to improve learning. efficiency, while also further promoting the development and application of emotional education.

References

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Published

2023-06-15

How to Cite

Wang, H., Li, D., Xu, R., Zhang, H., Liu, Y., Li, B. (2023). Expression Recognition System Based On Convolutional Neural Network. Eurasia Journal of Science and Technology, 1(1), 28-34. https://doi.org/10.61784/wjit231105