Online meetings have become a ubiquitous medium for communication in this contemporary era of remote work and virtual collaboration. However, ensuring active and focused participation in these virtual interactions poses a unique set of challenges. One crucial aspect that significantly impacts the effectiveness of online meetings is the level of participant attention and engagement and the overall performance of participants. Traditional methods of gauging attention, such as verbal cues or feedback, may not suffice in the digital realm. The benefits of enhanced accessibility and affordability, as well as the capability to overcome geographic limitations, have been extensively studied. Not only do these platforms facilitate efficient communication and cooperation but also offer a versatile platform for conducting interviews and hosting seminars, promoting a globally connected and interdependent workspace. We reviewed a system that restricts us from using a boosting algorithm to train a classifier capable of processing facial images efficiently with high detection accuracy. The core approach involves AdaBoost, a robust learning algorithm that constructs a powerful classifier by selecting visual features from a set of simple classifiers and linearly combining them.