Dozing Off or Thinking Hard?: Classifying Multi-dimensional Attentional States in the Classroom from Video

Abstract

In this paper, we extract features of head pose, eye gaze, and facial expressions from video to estimate individual learners' attentional states in a classroom setting. We concentrate on the analysis of different definitions for a student's attention and show that available generic video processing components and a single video camera are sufficient to estimate the attentional state.