  Following the increasingly popular trend of social interaction analysis in
egocentric vision, this manuscript presents a comprehensive study for automatic
social pattern characterization of a wearable photo-camera user, by relying on
the visual analysis of egocentric photo-streams. The proposed framework
consists of three major steps. The first step is to detect social interactions
of the user where the impact of several social signals on the task is explored.
The detected social events are inspected in the second step for categorization
into different social meetings. These two steps act at event-level where each
potential social event is modeled as a multi-dimensional time-series, whose
dimensions correspond to a set of relevant features for each task, and LSTM is
employed to classify the time-series. The last step of the framework is to
characterize social patterns, which is essentially to infer the diversity and
frequency of the social relations of the user through discovery of recurrences
of the same people across the whole set of social events of the user.
Experimental evaluation over a dataset acquired by 9 users demonstrates
promising results on the task of social pattern characterization from
egocentric photo-streams.
