Automatic detection of the level of human interest is of high relevance for many technical applications, such as automatic customer care or tutoring systems. However, the recognition of spontaneous interest in natural conversations independently of the subject remains a challenge. Identification of human affective states relying on single modalities only is often impossible, even for humans, since different modalities contain partially disjunctive cues. Multimodal approaches to human affect recognition generally are shown to boost recognition performance, yet are evaluated in restrictive laboratory settings only. Herein we introduce a fully automatic processing combination of Active-Appearance-Model-based facial expression, vision-based eyeactivity estimation, acoustic features, linguistic analysis, non-linguistic vocalisations, and temporal context information in an early feature fusion process. We provide detailed subject-independent results for classification and regression of the Level of Interest using Support-Vector Machines on an audiovisual interest corpus (AV IC) consisting of spontaneous, conversational speech demonstrating "theoretical" effectiveness of the approach. Further, to evaluate the approach with regards to real-life usability a user-study is conducted for proof of "practical" effectiveness.

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This paper introduces a new visual tracking technique combining particle filtering and Dynamic Bayesian Networks. The particle filter is utilized to robustly track an object in a video sequence and gain sets of descriptive object features. Dynamic Bayesian Networks use feature sequences to determine different motion patterns. A Graphical Model is introduced, which combines particle filter based tracking with Dynamic Bayesian Network-based classification. This unified framework allows for enhancing the tracking by adapting the dynamical model of the tracking process according to the classification results obtained from the Dynamic Bayesian Network. Therefore, the tracking step and classification step form a closed trackingclassification-tracking loop. In the first layer of the Graphical Model a particle filter is set up, whereas the second layer builds up the dynamical model of the particle filter based on the classification process of the Dynamic Bayesian Network.