User-centric speaker report: Ranking-based effectiveness evaluation and feedback

In this paper, we present a computer vision-based system that is capable of automatically analyzing presentation videos and evaluating it according to the learned user's preference. In this system, different visual features indicating the effectiveness of the presentation are extracted. They include the speaker's global movement, face/head orientation distribution and motions caused by the use of hands. Given a set of user scored presentation videos, we adapt the RankBoost [13] algorithm to learn the user's scoring preference so that the system can score a new presentation video in the future to provide the user feedback. The experiment results show that the vision processing part can reliably extract the low level features and the ranking learning part can successfully learn user's different scoring preferences and achieve an average ranking error within one level or less.