Dennis Küster

TL;DR: Testing eight out-of-the-box automatic classifiers for facial affect recognition revealed a recognition advantage for human observers over automatic classification, and the need for more spontaneous facial databases that can act as a benchmark in the training and testing of automatic emotion recognition systems.

TL;DR: The existing corpora are reviewed and the key dimensions and properties of the available sets are described, including a discussion of conceptual features in terms of thematic issues in dataset construction as well as practical features which are of applied interest to stimulus usage.

TL;DR: The results suggest that a damping procedure to reduce sudden large changes in sentiment can improve classification accuracy but that the optimal procedure will depend on the type of texts processed.

TL;DR: Insight from the literature is extended to include tools from user-centered design and analyses of human-human interaction as the basis of a multidisciplinary approach in the development of an empathic robotic tutor.

TL;DR: Recognition performance by the machine was found to be superior for posed expressions containing prototypical facial patterns, and comparable to humans when classifying emotions from spontaneous displays, suggesting that automated systems rely on expression prototypicality for emotion classification and may perform just as well as humans when tested in a cross-corpora context.