Towards a minimal representation of affective gestures (Extended abstract)

How efficiently decoding affective information when computational resources and sensor systems are limited? This paper presents a framework for analysis of affective behavior starting with a reduced amount of visual information related to human upper-body movements. The main goal is to individuate a minimal representation of emotional displays based on non-verbal gesture features. The GEMEP (Geneva multimodal emotion portrayals) corpus was used to validate this framework. Twelve emotions expressed by ten actors form the selected data set of emotion portrayals. Visual tracking of trajectories of head and hands was performed from a frontal and a lateral view. Postural/shape and dynamic expressive gesture features were identified and analyzed. A feature reduction procedure was carried out, resulting in a four-dimensional model of emotion expression, that effectively classified/grouped emotions according to their valence (positive, negative) and arousal (high, low). These results show that emotionally relevant information can be detected/measured/obtained from the dynamic qualities of gesture. The framework was implemented as software modules (plug-ins) extending the EyesWeb XMI Expressive Gesture Processing Library and was tested as a component for a multimodal search engine in collaboration with Google within the EU-ICT I-SEARCH project.