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Sensing, Interpreting, and Anticipating Human Social Behaviour in the Real World

Philipp Müller
Max-Planck-Institut für Informatik - D2
AG 1, AG 3, AG 4, RG1, MMCI, AG 2, INET, AG 5, SWS  
Public Audience

Date, Time and Location

Friday, 13 November 2020
60 Minutes


A prerequisite for the creation of social machines that are able to support humans in fields like education, psychotherapy, or human resources is the ability to automatically detect and analyse human nonverbal behaviour. While promising results have been shown in controlled settings, automatically analysing unconstrained situations, e.g. in daily-life settings, remains challenging.

This thesis moves closer to the vision of social machines in the real world, making fundamental contributions along the three dimensions of sensing, interpreting and anticipating nonverbal behaviour in social interactions. First, it advances the state of the art in human visual behaviour sensing, proposing a novel unsupervised method for eye contact detection in group interactions by exploiting the connection between gaze and speaking turns. Furthermore, the thesis makes use of mobile device engagement to address the problem of calibration drift that occurs in daily-life usage of mobile eye trackers. Second, this thesis improves the interpretation of social signals by proposing datasets and methods for emotion recognition and low rapport detection in less constrained settings than previously studied. In addition, it for the first time investigates a cross-dataset evaluation setting for emergent leadership detection. Third, this thesis pioneers methods for the anticipation of eye contact in dyadic conversations, as well as in the context of mobile device interactions during daily life, thereby paving the way for interfaces that are able to proactively intervene and support interacting humans.


Connie Balzert
+49 681 9325 2000
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Connie Balzert, 10/30/2020 11:25
Connie Balzert, 10/21/2020 11:52 -- Created document.