Face Clustering for Connection Discovery from Event Images

TL;DR

This paper presents a face clustering system that discovers social connections from event images, enabling social graph construction without user input or online social network access, with promising clustering accuracy.

Contribution

It introduces a novel face clustering method for event images that enables social connection discovery without participant identities or online social network data.

Findings

Achieved 80% F1 score in face clustering accuracy.

Successfully constructed social graphs from collected event images.

Demonstrated potential for offline social network analysis.

Abstract

Social graphs are very useful for many applications, such as recommendations and community detections. However, they are only accessible to big social network operators due to both data availability and privacy concerns. Event images also capture the interactions among the participants, from which social connections can be discovered to form a social graph. Unlike online social graphs, social connections carried by event images can be extracted without user inputs, and hence many social graph-based applications become possible, even without access to online social graphs. This paper proposes a system to discover social connections from event images. By utilizing the social information from even images, such as co-occurrence, a face clustering method is proposed and implemented, and connections can be discovered without the identity of the event participants. By collecting over 40000 faces from over 3000 participants, it is shown that the faces can be well clustered with 80% in F1 score, and social graphs can be constructed. Utilizing offline event images may create a long-term impact on social network analytics.