Application: Facial Expression Recognition

Abstract

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 compare the different approaches of the previous chapters for the design of a facial expression recognition system. Our experiments suggest that using the TAN classifiers and the stochastic structure search algorithm described in Chapter 7 outperform previous approaches using Bayesian network classifiers, or even compared to Neural networks. We also show experimentally that the learning the structure with the SSS algorithm holds the most promise when learning to classify facial expressions with labeled and unlabeled data.