Evidence theory based multimodal emotion recognition

Paleari, Marco;Benmokhtar, Rachid;Huet, Benoit
MMM 2009, 15th International MultiMedia Modeling Conference, January 7-9, 2009, Sophia Antipolis, France

 

 

 

Automatic recognition of human affective states is still a largely unexplored and challenging topic. Even more issues arise when dealing with variable quality of the inputs or aiming for real-time, unconstrained, and person independent scenarios. In this paper, we explore audio-visual multimodal emotion recognition. We present SAMMI, a framework designed to extract real-time emotion appraisals from non-prototypical, person independent, facial expressions and vocal prosody. Different probabilistic method for fusion are compared and evaluated with a novel fusion technique called NNET. Results shows that NNET can improve the recognition score (CR+) of about 19% and the mean average precision of about 30% with respect to the best unimodal system.


DOI
Type:
Conférence
City:
Sophia Antipolis
Date:
2009-01-07
Department:
Data Science
Eurecom Ref:
2596
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in MMM 2009, 15th International MultiMedia Modeling Conference, January 7-9, 2009, Sophia Antipolis, France and is available at : http://dx.doi.org/10.1007/978-3-540-92892-8_44

PERMALINK : https://www.eurecom.fr/publication/2596