Tracking audience engagement in real-time offers numerous benefits. For instance, event planners can make dynamic adjustments to presentations or activities to maintain high levels of interest and participation. This enhances the overall experience for attendees by ensuring the content remains engaging and relevant. This paper proposes a model for computing the binary engagement within groups. The model does this by identifying individuals’ engagement during the events’ time frames, which are then combined, i.e., the engagement of the group is computed by aggregating the engagement of each individual. For each individual of the group, the engagement model incorporates the computation over time of the gaze direction, valence, and arousal, classifying the engagement into two primary levels: not-engaged and engaged. The engaged category is further divided into two sublevels: positive and negative engagement. Experimental results confirm the model’s effectiveness, showcasing reliable identity tracking and accurate assessment of engagement states in dynamic scenarios.