Current Journal of Applied Science and Technology

The automated analysis of facial expression is a long investigated subject in the computer vision community and has been boosted by applications in the field of human computer interaction (HCI). Besides mapping of facial expressions to basic emotion categories, what is often of limited use for HCI due to sparse occurrence of real emotions, other approaches have been proposed to transform facial expression to the two dimensional so-called valence arousal space. With these affective user state parameters available, the course of the interaction can basically be guided smarter, i.e. the computer can provide help to an apparently confused user. However, it has been shown that the valence arousal space transformation can be impaired due to inaccuracies in image based feature extraction. In this article we present an advanced method using image processing and 3-D computer vision technology that on the one hand suppresses this problem through hierarchical analysis. Further, our concept enables the assignment of an intensity level of the affective state, which can be a valuable parameter for the interaction. In this paper we give details on the system concept with the different processing steps and respective results. By the application of our method we achieve improvement of facial expression recognition compared to other state-of-the-art methods. In particular we can distinguish roughly 15 percent more classes while maintaining the high recognition rate.