Affiliations: [a] College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, Gansu Province, China
| [b] Department of Radiation Oncology, Gansu Provincial Hospital, Lanzhou, Gansu Province, China
Correspondence:
[*]
Corresponding author. Xiangyu Deng, College of Physics and Electronic Engineering, Northwest Normal University, No. 967, Anning East Road Lanzhou, Gansu Province, China. E-mail: [email protected].
Abstract: With the development of artificial intelligence technology, the digital transformation of student-oriented education becomes particularly important. How to promote real-time interaction between teachers and students in the classroom is an urgent issue which is needed to pay attention to. Based on the facial expression features of students in a classroom, this paper analyzes the changes in angles between facial expression feature points using Dlib. Additionally, this paper proposes a novel algorithm for extracting variable scale template edge trend features. The algorithm adaptively processes the template based on the edge trend features of expression feature points, and use the proposed template slope normalization algorithm to achieve multi-scale template edge trend extraction. Then, DNN are used to recognize different listening expressions. The experimental results show that the proposed algorithm has faster recognition speed and better robustness when applied to classroom expression recognition. By identifying students’ class status to remind teachers to adjust their class progress, the goal of improving classroom learning effectiveness is achieved
