Author(s)

Huihui Wang, Dan Li^*, Ruiqun Xu, Hengjia Zhang, Yi Liu, Bohua Li

Affiliation(s)

Xuzhou University of Technology, Xuzhou, Jiangsu, China.

Corresponding Author

Dan Li

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.

KEYWORDS

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

CITE THIS PAPER

Huihui Wang, Dan Li, Ruiqun Xu, Hengjia Zhang, Yi Liu, Bohua Li. Expression recognition system based on convolutional neural network. World Journal of Information Technology. 2023, 1(1): 28-34. DOI: 10.61784/wjit231105.

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