Human-computer interaction (HCI) lies at the crossroads of many scientific areas including artificial intelligence, computer vision, face recognition, motion tracking, etc. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for human-computer interaction applications. We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classificaion performance and we investigate the implications of this analysis to a specifie 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. © 2005 IEEE.
Human-Computer Interaction: A Bayesian Network Approach
Sebe, Niculae;
2005-01-01
Abstract
Human-computer interaction (HCI) lies at the crossroads of many scientific areas including artificial intelligence, computer vision, face recognition, motion tracking, etc. In this paper, we discuss training probabilistic classifiers with labeled and unlabeled data for human-computer interaction applications. We provide an analysis which shows under what conditions unlabeled data can be used in learning to improve classificaion performance and we investigate the implications of this analysis to a specifie 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. © 2005 IEEE.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione
