Estimation of students' attention in the classroom from kinect features

Abstract

This paper proposes a novel approach to automatic estimation of attention of students during lectures in the class-room. The approach uses 2D and 3D features obtained by the Kinect One sensor characterizing both facial and body properties of a student, including gaze point and body posture. Machine learning algorithms are used to train attention model, providing classifiers which estimate attention level of individual student. Human encoding of attention level is used as a training set data. The experiment included 3 persons whose attention was annotated over 4 minute period in a resolution of 1 second. We review available Kinect features and propose features matching the visual attention and inattention cues, and present the results of classification experiments.