Exploring album structure for face recognition in online social networks

•We propose a face recognition model based on albums in online social networks.•Albums tend to be composed of many pictures of a small group of people.•Limiting the set of unique labels and using album-level features improve recognition.•We present two systems to learn how best to use these social features.•We validate our model on two datasets independently downloaded from Facebook.

In this paper, we propose an album-oriented face-recognition model that exploits the album structure for face recognition in online social networks. Albums, usually associated with pictures of a small group of people at a certain event or occasion, provide vital information that can be used to effectively reduce the possible list of candidate labels. We show how this intuition can be formalized into a model that expresses a prior on how albums tend to have many pictures of a small number of people. We also show how it can be extended to include other information available in a social network. Using two real-world datasets independently drawn from Facebook, we show that this model is broadly applicable and can significantly improve recognition rates.

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