Abstract
Current approaches to emotion recognition do not address the fact that emotions are dynamic processes. This work concerns itself with the development of a gray-box framework for dynamic emotion intensity estimation that can incorporate findings from appraisal models, specifically Scherer's Component Process Model. It is based on Dynamic Field Theory which allows the combination of theoretical knowledge with data-driven experimental approaches. Further, we conducted an exemplary user study applying the proposed model to estimate intensity of negative emotions from physiological signals. Results show significant improvements of the proposed model to common methodology and baselines. The flexible cognitive architecture opens a wide field of experiments and directions to deepen the understanding of emotion processes as a whole.