Learning probabilistic classifiers for human-computer interaction applications

Human-computer interaction (HCI) lies at the crossroads of many scientific areas including artificial intelligence, computer vision, face recognition, motion tracking, etc. It is argued that to truly achieve effective human-computer intelligent interaction, the computer should be able to interact naturally with the user, similar to the way HCI takes place. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for HCI applications. We provide an analysis that shows under what conditions unlabeled data can be used in learning to improve classification performance, and we investigate the implications of this analysis to a specific type of probabilistic classifiers, Bayesian networks. Finally, we show how the resulting algorithms are successfully employed in facial expression recognition, face detection, and skin detection.