Principled Hybrid Systems:  Theory and Applications

Systematic principles for integrating symbolic and subsymbolic processing will be developed in the project. Key aims are to ensure that the resulting total hybrid system retains desirable properties of both processing levels. On the one side the signal processing abilities, robustness and learning capability of neural networks should be preserved. On the other side advantage should be taken of the ability of rule-based systems to exploit high level knowledge and existing algorithms and to explain (to a user) why conclusions were reached in particular cases. The methodologies to be developed in the project will be tested in a challenging application related to human computer interaction, which is recognition of emotion based on both voice and visual cues. Low level features will be extracted from signals using neural networks and subsequent formulation of rules will provide a conceptual framework, substantial for emotion analysis.
See DEMO of the facial features extraction procedure.