Georg Layher

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In this contribution we extend existing methods for head pose estimation and investigate the use of local image phase for gaze detection. Moreover we describe how a small database of face images with given ground truth for head pose and gaze direction was acquired. With this database we compare two different computational approaches for extracting the head(More)
The analysis of affective or communicational states in human-human and human-computer interaction (HCI) using automatic machine analysis and learning approaches often suffers from the simplicity of the approaches or that very ambitious steps are often tried to be taken at once. In this paper, we propose a generic framework that overcomes many difficulties(More)
The detection and categorization of animate motions is a crucial task underlying social interaction and perceptual decision-making. Neural representations of perceived animate objects are built in the primate cortical region STS which is a region of convergent input from intermediate level form and motion representations. Populations of STS cells exist(More)
It appears that the mirror neuron system plays a crucial role when learning by imitation. However, it remains unclear how mirror neuron properties develop in the first place. A likely prerequisite for developing mirror neurons may be the capability to transform observed motion into a sufficiently self-centered frame of reference. We propose an artificial(More)
We investigate the influence of audiovisual features on the perception of speaking style and performance of politicians, utilizing a large publicly available dataset of German parliament recordings. We conduct a human perception experiment involving eye-tracker data to evaluate human ratings as well as behavior in two separate conditions, i.e. audiovisual(More)
Although an action observation network and mirror neurons for understanding the actions and intentions of others have been under deep, interdisciplinary consideration over recent years, it remains largely unknown how the brain manages to map visually perceived biological motion of others onto its own motor system. This paper shows how such a mapping may be(More)
How do we manage to step into another person's shoes and eventually derive the intention behind observed behavior? We propose a connectionist neural network (NN) model that learns self-supervised a prerequisite of this social capability: it adapts its internal perspective in accordance to observed biological motion. The model first learns predictive(More)