Towards real-time affect detection based on sample entropy analysis of expressive gesture
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
The aMotion toolkit: painting with affective motion textures
CAe '12 Proceedings of the Eighth Annual Symposium on Computational Aesthetics in Graphics, Visualization, and Imaging
I-SEARCH: a unified framework for multimodal search and retrieval
The Future Internet
Computing and evaluating the body laughter index
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
Image and Vision Computing
Image and Vision Computing
Natural interaction expressivity modeling and analysis
Proceedings of the 6th International Conference on PErvasive Technologies Related to Assistive Environments
Hi-index | 0.00 |
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 nonverbal gesture features. The GEMEP (Geneva multimodal emotion portrayals) corpus was used to validate this framework. Twelve emotions expressed by 10 actors form the selected data set of emotion portrayals. Visual tracking of trajectories of head and hands were 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 4D 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 is going to be used in user centric, networked media applications, including future mobiles, characterized by low computational resources, and limited sensor systems.