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- W4377822010 abstract "In Distance English teaching, teachers cannot take care of every student because of their own limitations. Therefore, more advanced technology is needed to assist teachers in completing teaching. Based on this point, this paper proposes a distance English teaching assistant method based on facial expression recognition technology. First, the distance learning platform collects the image data of students through the camera, preprocesses the captured image, detects and recognizes the skin color, and finally determines the position of the face. Then, based on the theory of genetic algorithm, a model to improve the characteristics of face images is established, and the recognized facial expression images are classified by random forest algorithm, and the facial expression characteristics of students are analyzed according to the five kinds of information of the target, such as attention, suspicion, distraction, excitement and anxiety. This paper adds the design of expression recognition application to the English learning distance platform, collects and summarizes the changes in students' expressions through the mobile terminal, and reports them to the teacher as reference information in the teaching process. The results show that the facial expression recognition module carried on the distance English teaching platform has a relatively accurate recognition rate, can provide some help for English teachers, and is well received by teachers, so it is worth promoting. By studying genetic algorithm and facial expression recognition technology, this paper applies them to the design of English distance teaching platform, which has a certain auxiliary effect on English teachers' teaching behavior." @default.
- W4377822010 created "2023-05-24" @default.
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- W4377822010 date "2023-05-23" @default.
- W4377822010 modified "2023-10-16" @default.
- W4377822010 title "Application of expression recognition based on genetic algorithm in English distance teaching platform" @default.
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- W4377822010 doi "https://doi.org/10.1007/s00500-023-08423-2" @default.
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