Online learning has undoubtedly increased in popularity in the past few years. The COVID-19 pandemic has further accelerated the alteration to online education and increased the need for secure methods to authenticate and verify online students. Today, several technologies offer varying degrees of automation. This paper presents a comprehensive analysis of a specific solution that integrates multiple automated authentication technologies with an automated verification system. The parameters we used to achieve our goal are face detection, eye tracking, multiple-person detection, emotion detection, distance estimation, and background noise detection. All of these components help maintain the integrity of the exam and mitigate the existing limitations of e-exam acceptance software.