Towards Egocentric Person Re-identification and Social Pattern Analysis
This work addresses the lack of tools for monitoring social interactions and patterns for users of wearable cameras, but it is incremental as it builds on existing clustering and re-identification methods.
The paper tackled the problem of analyzing social interactions from egocentric camera data by detecting and re-identifying faces to infer social patterns, and validated the model over several weeks with findings indicating potential usefulness for social behavior interpretation.
Wearable cameras capture a first-person view of the daily activities of the camera wearer, offering a visual diary of the user behaviour. Detection of the appearance of people the camera user interacts with for social interactions analysis is of high interest. Generally speaking, social events, lifestyle and health are highly correlated, but there is a lack of tools to monitor and analyse them. We consider that egocentric vision provides a tool to obtain information and understand users social interactions. We propose a model that enables us to evaluate and visualize social traits obtained by analysing social interactions appearance within egocentric photostreams. Given sets of egocentric images, we detect the appearance of faces within the days of the camera wearer, and rely on clustering algorithms to group their feature descriptors in order to re-identify persons. Recurrence of detected faces within photostreams allows us to shape an idea of the social pattern of behaviour of the user. We validated our model over several weeks recorded by different camera wearers. Our findings indicate that social profiles are potentially useful for social behaviour interpretation.