Design of a new online three-dimensional interactive teaching system for Agricultural and Forestry Colleges

Abstract

Face recognition technology has garnered significant research interest over the years, with various techniques focusing on low resolution, high resolution, and non-face features. This study delves into the development of teaching high-resolution face recognition, particularly in the context of agricultural and forestry colleges. The aim is to enhance the performance of this technique using advanced materials and processing algorithms, with a specific focus on mitigating degradation caused by illumination conditions and face occlusions. To improve the effectiveness of classroom teaching in these specialized colleges, this research analyzes the monitoring of classroom activities. The paper proposes the integration of high-resolution algorithms and EDF (Enhanced Data Format) image reconstruction technology into the College Chinese classroom teaching system. By applying high-resolution algorithms and advanced image processing techniques, the goal is to achieve improved resolution for students' facial features during the teaching process. To further enhance the system's capabilities, a fast pixel capture function is introduced to improve the video capture speed. A crucial aspect of this process is the velocity analysis, which involves stack velocity picking of speed spectrum. The paper employs optimization algorithms to enhance the stack velocity picking speed, thus achieving more effective algorithm improvements. Building on these advancements, the paper constructs a robust system structure and designs simulation experiments to validate the system's performance. Through experimental research, the study demonstrates that the College Chinese classroom teaching system developed in this work can accurately recognize the real-time status of students through face recognition. Consequently, it effectively improves the overall teaching and learning experience in agricultural and forestry colleges. This research opens up new possibilities for implementing high-resolution face recognition technology in specialized educational settings. By leveraging these advancements, agricultural and forestry colleges can create more efficient and engaging classroom environments, enhancing the overall teaching effectiveness and contributing to improved learning outcomes for students.