Automated facial affect analysis for one-on-one tutoring applications

In this paper, we explore the use of computer vision techniques to analyze studentsu0027 moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the studentu0027s mood and affective state. We find that the problem of accurately determining a childu0027s mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.