Social Style Characterization from Egocentric Photo-streams
This work addresses social pattern analysis for individuals using egocentric cameras, but it is incremental as it builds on existing methods for interaction detection and classification.
The paper tackles the problem of automatically characterizing social patterns from wearable camera photo-streams by detecting interactions, categorizing them as formal or informal using LSTM networks, and clustering person recurrences. Experiments on a month-long dataset show promising results.
This paper proposes a system for automatic social pattern characterization using a wearable photo-camera. The proposed pipeline consists of three major steps. First, detection of people with whom the camera wearer interacts and, second, categorization of the detected social interactions into formal and informal. 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 a LSTM network is employed for time-series classification. In the last step, recurrences of the same person across the whole set of social interactions are clustered to achieve a comprehensive understanding of the diversity and frequency of the social relations of the user. Experiments over a dataset acquired by a user wearing a photo-camera during a month show promising results on the task of social pattern characterization from egocentric photo-streams.