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Recognition of emotions from multimodal cues is of basic interest for the design of many adaptive interfaces in human-machine interaction (HMI) in general and human-robot interaction (HRI) in particular. It provides a means to incorporate non-verbal feedback in the course of interaction. Humans express their emotional and affective state rather unconsciously exploiting their different natural communication modalities such as body language, facial expression and prosodic intonation. In order to achieve applicability in realistic HRI settings, we develop person-independent affective models. In this paper, we present a study on multimodal recognition of emotions from such auditive and visual cues for interaction interfaces. We recognize six classes of basic emotions plus the neutral one of talking persons. The focus hereby lies on the simultaneous online visual and accoustic analysis of speaking faces. A probabilistic decision level fusion scheme based on Bayesian networks is applied to draw benefit of the complementary information from both – the acoustic and the visual – cues. We compare the performance of our state of the art recognition systems for separate modalities to the improved results after applying our fusion scheme on both DaFEx database and a real-life data that captured directly from robot. We furthermore discuss the results with regard to the theoretical background and future applications.
To reduce the number of traffic accidents and to increase the drivers comfort, the thought of designing driver assistance systems arose in the past years. Fully or partly autonomously guided vehicles, particularly for road traffic, pose high demands on the development of reliable algorithms. Principal problems are caused by having a moving observer in predominantly natural environments. At the Institut fur Neuroinformatik methods for analyzing driving relevant scenes by computer vision are developed in cooperation with several partners from the automobile industry. We present a solution for a driver assistance system. We concentrate on the aspects of video-based scene analysis and organization of behavior.
Fahrerassistenzsysteme werden eingesetzt, um dem Fahrer
eines Kraftfahrzeugs Handlungsabläufe abzunehmen. Diese Handlungsabläufe
werden definiert durch eine Aufgabenstellung, die vom Fahrer an das Fahrerassi-
stenzsystem übergeben oder systembedingt gelöst wird. Bei komplexen Fahreras-
sistenzsystemen ist an eine autonome Navigation im Straßenverkehr gedacht. Es
wird ein neues Verfahren vorgestellt, welches eine Bewegungssteuerung eines
autonomen Fahrzeugs durchführen kann. Es werden der Lenkwinkel und die Ge-
schwindigkeit beeinflußt. Für diese Aufgabe wird ein dynamischer Ansatz aus
dem Bereich der neuronalen Felder gewählt. Relevante Attribute für den Fahrt-
verlauf auf unterschiedlichem Abstraktionsniveau können dabei einfach (additiv)
verarbeitet werden.