Scalable face labeling in online social networks

Face labeling is the process of assigning names to faces. In this paper, we start from a weakly-supervised setting where names are linked to photos, not faces. We introduce two face labeling strategies that scale well to large data sets and allow for labeling parts thereof. This is a useful property especially for data sets where photos are frequently added or (re)labeled. We evaluate our and two related face labeling strategies on a novel corpus of 34,763 faces, gathered from an online social network for dance party visitors. We achieve a speed-up of an order of magnitude over the state-of-the-art approach while the labeling quality is almost unaffected. On a subset of the faces, the speed-up is even more apparent, reaching at least two orders of magnitude.