Intelligent Classroom Teaching Assessment System Based on Deep Learning Model Face Recognition Technology
Xiaona Zhang, Xiaoming Zhang, Ts. Dr Jasni Bin Dolah
Abstract
AI + education technology has had a wide and far-reaching impact on the education industry, which has been applied to classroom teaching in order to achieve real-time monitoring of the classroom and complete closed-loop feedback on teaching. In this paper, AI face recognition based on deep learning is improved and optimized through key points such as image data acquisition, localization, calibration, comparison, and deepening data models, so that the detection can be completed accurately and quickly even under the condition of light and external object interference, maximizing the accuracy and efficiency of face recognition technology under the influence of the objective environment. The traditional manual operation in classroom teaching is automated and intelligent throughout, reducing the workload of teachers and related personnel while further ensuring the scientific organization and management of classroom teaching. While making classroom teaching and learning life safer, more convenient, and more efficient, it also provides a favourable reference for the wide application and development of facial recognition technology.