Adaptation of facial feature extraction and rule generation in emotion-analysis systems
The paper addresses the problem of emotion recognition in faces through an intelligent neuro-fuzzy system, where the extraction of facial features follows the MPEG-4 standard and is adapted to particular environmental conditions and specific persons. These features are associated to symbolic fuzzy predicates providing the classification of facial images according to the underlying emotional states. For this classification we use rules extracted from psychological studies and expression databases including extreme expressions such as those illustrated in Ekman's database. The rules are then refined in realistic conditions, taking into account the extracted features. The experimental results, based both in extreme and naturalistic databases developed in the frameworks of IST ERMIS and NoE HUMAINE, illustrate the capability of the developed system to analyse and recognise facial expressions in human computer interaction applications.