Design and Data Analysis of Online Certification System for English Courses based on Face Recognition Algorithm

This article outlines the design and data analysis of an advanced online English course certification system using facial recognition algorithms. It traces the evolutionary stages of facial recognition technology, emphasizing its widespread integration into real-world applications. The proposed framework employs cutting-edge algorithms to enhance learner identity verification by capturing and matching distinctive facial features with stored data for improved accuracy and fraud prevention. The integration of data analytics captures various facets of the learner experience, promoting security and usability in online English learning beyond traditional methods. The paper presents a novel Collaborative Representation Classification (COR) algorithm that minimizes reconstruction errors and efficiently categorizes samples. The proposed face recognition model incorporates CenterLoss for feature optimization, which enhances performance across datasets. The online certification system, built on the Hibernate framework, includes user, authentication proxy server, and online identity authentication server components, ensuring adaptability to different online English learning contexts. A case study highlights the relevance of the system in the teaching process, emphasizing effective data transfer in remote courses. The article concludes with systematic verification, evaluating face recognition accuracy using the ORL and Yale face databases. Experimental results show variability in the accuracy of training samples, indicating the need for targeted optimization. The article recommends future research to refine the certification system for different online learning scenarios.