Machine learning techniques for face analysis

In recent years there has been a growing interest in improving all aspects of the interaction between humans and computers with the clear goal of achieving a natural interaction, similar to the way human-human interaction takes place. The most expressive way humans display emotions is through facial expressions. Humans detect and interpret faces and facial expressions in a scene with little or no effort. Still, development of an automated system that accomplishes this task is rather difficult. There are several related problems: detection of an image segment as a face, extraction of the facial expression information, and classification of the expression (e.g., in emotion categories). A system that performs these operations accurately and in real time would be a major step forward in achieving a human-like interaction between the man and machine. In this chapter, we present several machine learning algorithms applied to face analysis and stress the importance of learning the structure of Bayesian network classifiers when they are applied to face and facial expression analysis.