THE ROLE OF MACHINE LEARNING ALGORITHMS IN ENHANCING THE ACCURACY AND THE RELIABILITY OF FACIAL IDENTITY, VERIFICATION IN ONLINE ASSESSMENT

This study scrutinizes exactly how machine learning algorithms can considerably advance the accuracy and reliability of facial identity verification systems used in online examinations. the paper examines how different machine learning (ML) methods are integrated into AI-powered facial recognition systems and assess how well they work to improve accuracy and resilience through a thorough investigation. The algorithms’ effectiveness was evaluated in several contexts and the way they might advance safe user authentication in online learning settings. This study shed more light the innovative potential of machine learning in strengthening the safety and integrity of online examinations by examining existing developments and addressing future concerns. Furthermore, we contribute to the discussion around machine learning in learning technology by emphasizing the importance of the underlying further promoting accuracy and reliability. By applying ML technologies, one may achieve a reliable and organized verification process in the digital learning environment (DLE). To wrap up, this study emphasizes the importance of such machine learning achievements as a more reliable and safe ground for online testing. This achievement will contribute to better education and academic activity with regard to the scholarly integrity.