Face Clustering for Connection Discovery from Event Images
Ming Cheung
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.