Evaluating AAM fitting methods for facial expression recognition

Abstract

The human face is a rich source of information for the viewer and facial expressions are a major component in judging a person's affective state, intention and personality. Facial expressions are an important part of human-human interaction and have the potential to play an equally important part in human-computer interaction. This paper evaluates various Active Appearance Model (AAM) fitting methods, including both the original formulation as well as several state-of-the-art methods, for the task of automatic facial expression recognition. The AAM is a powerful statistical model for modelling and registering deformable objects. The results of the fitting process are used in a facial expression recognition task using a region-based intermediate representation related to Action Units, with the expression classification task realised using a Support Vector Machine. Experiments are performed for both person-dependent and person-independent setups. Overall, the best facial expression recognition results were obtained by using the Iterative Error Bound Minimisation method, which consistently resulted in accurate face model alignment and facial expression recognition even when the initial face detection used to initialise the fitting procedure was poor.