Scalable multimodal fusion for continuous affect sensing

The success of affective interfaces lies in the fusion of emotional information coming from different modalities. This paper proposes a scalable methodology for fusing multiple affect sensing modules, allowing the subsequent addition of new modules without having to retrain the existing ones. It relies on a 2-dimensional affective model and is able to output a continuous emotional path characterizing the user's affective progress over time.