Description
Domains where human behavior understanding is a crucial need (e.g., human-computer interaction, affective computing and social signal processing) rely on advanced pattern recognition techniques to automatically interpret complex behavioral patterns generated when humans interact with machines or with others. This is a challenging problem where many issues are still open, including the joint modeling of behavioral cues taking place at different time scales, the inherent uncertainty of machine detectable evidences of human behavior, the mutual influence of people involved in interactions, the presence of long term dependencies in observations extracted from human behavior, and the important role of dynamics in human behavior understanding.
This workshop will gather researchers dealing with the problem of modeling human behavior under its multiple facets (expression of emotions, display of relational attitudes, performance of individual or joint actions, etc.), with particular attention to pattern recognition approaches that involve multiple modalities and those that model the actual dynamics of behavior. The contiguity with ICPR, one of the most important events in the Pattern Recognition and Machine Learning communities, is expected to foster cross-pollination with other areas, e.g. temporal pattern mining or time series analysis, which share their important methodological aspects with human behavior understanding. Furthermore, the presence of this workshop at ICPR is expected to attract researchers (in particular PhD students and postdoctoral researchers) to a domain like human behavior understanding that is likely to play a major role in future technology (ambient intelligence, human-robot interaction, artificial social intelligence, etc.), as witnessed by a number of research efforts aimed at collecting and annotating large sets of multi-sensor data, collected from observing people in natural (often technologically challenging) conditions.