Evaluating AAM Fitting Methods for Facial Expression Recognition

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 im-portant 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 sta-tistical model for modelling and registering deformable ob-jects. The results of the fitting process are used in a facial expression recognition task using a region-based interme-diate representation related to Action Units, with the ex-pression classification task realised using a Support Vec-tor Machine. Experiments are performed for both person-dependent and person-independent setups. Overall, the best facial expression recognition results were obtained by us-ing the Iterative Error Bound Minimisation method, which consistently resulted in accurate face model alignment and facial expression recognition even when the initial face de-tection used to initialise the fitting procedure was poor. 1