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Real-time Engagement Detection from Facial Features

Nowadays, engagement detection plays an essential role in e-learning education and robotics. In the field of human-agent interaction, it is of great interest to know the attitude of the human peer towards the interaction so that the agent can react accordingly. The goal of this paper is to develop an automatic real-time engagement recognition system using a combination of non-verbal features (gaze direction, head position, facial expression and distance between users) extracted using computer vision techniques. Our system uses a machine learning model based on Random Forest and achieves 86% accuracy, improving the results of the state-of-the-art methods by 22.2% in engagement level detection accuracy on the Daisee dataset. Furthermore, using an RGB camera, the system can detect the level of user engagement in real-time and classify it into four levels of intensity.