Deep and Self-organizing Neural Networks for Affective Modeling

(2013-2016) University of Hamburg – Germany

Modeling affective mechanisms from humans in computer systems is a very difficult task, even with the recent advances of deep neural networks. This project focus on two different mechanisms of affective computing: perception and intrinsic modeling.  On the perception side, one of the crucial problems is the subjectivity of emotion description, as different persons can express and perceive emotions differently, depending on several contextual factors. On the intrinsic modeling side, the constraints on emotion representation and its modulation by perception and action must be taken into consideration. This project proposes a neural framework for dealing with emotion perception, modeling, and modulation over different affective mechanisms with the use of deep and self-organizing networks.

Data & Code

Gesture Commands for Robot inTeracton (GRIT)

 

Media

Barros, P., Strahl, E., Wermter, S. The iCub Chronicles – Attention to Emotions! AAAI Conference on Artificial Intelligence, Arizona, USA, 2016

 

Spontaneous Emotion Recognition

 

Real-time Gesture Recognition

 

Publications associated with this project

 

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