# Classroom Expression Recognition Based on Deep Learning
## 1 Introduction  
In recent years, the rapid development of technologies such as the Internet, artificial intelligence, and big data has helped usher in the era of educational informatization 2.0, and smart education has emerged as the times require. At present, online learning has been widely used in distance education, assisted classroom teaching, etc., providing a strong guarantee for promoting the reform of modern education. Online learning breaks the time and space constraints in the traditional teaching process and provides learners with a new form of learning. However, compared with traditional teaching forms, its shortcomings cannot be ignored. There is a lack of interaction between teachers and learners. Teachers cannot perceive learners' learning status and emotions. Students' emotions and expressions reflect their learning status and the degree of classroom participation, and teachers need to understand these emotional changes in time in order to adjust teaching strategies in a timely manner [1]. At present, facial recognition technology has achieved remarkable results in areas such as attendance and security, which provides new ideas for smart online education. Deep learning can analyze students' learning status, improve learning effects, and optimize teaching strategies [2]. Therefore, by capturing the completeness and expression changes of students' faces through expression recognition technology and counting and analyzingstudents' classroom concentration, teachers can understand and manage students' learning status and promote the improvement of teaching quality. This system innovatively combines deep learning with classroom expression recognition technology. By capturing and analyzing students' expressions, it accurately identifies students' emotional states such as concentration, confusion, and fatigue in the classroom. The system uses spatial attention, channel attention, and self-attention mechanisms to extract deep features from students' expression data and further optimizes the recognition accuracy of key areas through attention mechanisms. Teachers can adjust the teaching rhythm in a timely manner and enhance classroom interaction based on these recognition results. The system can not only objectively and accurately feedback students' emotional states but also provide personalized data support for education, enabling teachers to improve teaching methods more effectively. Through this system, students' emotions and learning states can be monitored, thereby achieving a more efficient and personalized classroom experience. In summary, student expression recognition in the classroom environment is of great significance and can provide better support for teachers' teaching and students' learning:  
1. Optimizing teaching strategies: Through student expression recognition in the classroom environment, teachers can timely understand students' emotional state and learning state, so as to adjust teaching strategies and make teaching more targeted and effective. 2. Improving teaching quality: Teachers can analyze students' learning behaviors, such as concentration and learning motivation, based on their expressions, so as to better help students learn and improve teaching quality. 3. Improving students' self-awareness and emotional management ability: Through expression recognition technology, students can better understand their emotional state and learning state, so as to timely adjust their learning state and improve their self-awareness and emotional management ability. 4. Promoting educational informatization: Student expression recognition in the classroom environment requires the help of advanced computer technology, which can promote the development of smart education informatization. However, with the widespread application of recognition technology, ethical issues have also become increasingly prominent. In particular, in terms of personal data protection, the regulations of different countries may vary.