Learning Bayesian Network Classifiers for Facial Expression Recognition using both Labeled and Unlabeled Data

Understanding human emotions is one of the necessary skills for the computer to interact intelligently with human users. The most expressive way humans display emotions is through facial expressions. In this paper, we report on several advances we have made in building a system for classification of facial expressions from continuous video input. We use Bayesian network classifiers for classifying expressions from video. One of the motivating factor in using the Bayesian network classifiers is their ability to handle missing data, both during inference and training. In particular, we are interested in the problem of learning with both labeled and unlabeled data. We show that when using unlabeled data to learn classifiers, using correct modeling assumptions is critical for achieving improved classification performance. Motivated by this, we introduce a classification driven stochastic structure search algorithm for learning the structure of Bayesian network classifiers. We show that wi...

• Type: Conference
• Year: 2003
• Where: CVPR
• Authors: Ira Cohen, Nicu Sebe, Fabio Gagliardi Cozman, Marcelo Cesar Cirelo, Thomas S. Huang
• Keywords: Bayesian Network Classifiers, Computer Vision, CVPR 2003, Labeled Training Sets, Naive Bayes, Size Labeled Training, Unlabeled Data