Family photo collections often contain richer semantics than arbitrary images of people because families contain a handful of specific individuals who can be associated with certain social roles (e.g. father, mother, or child). As a result, family photo collections have unique challenges and opportunities for face recognition compared to random groups of photos containing people. We address the problem of unsupervised family member discovery: given a collection of family photos, we infer the size of the family, as well as the visual appearance and social role of each family member. As a result, we are able to recognize the same individual across many different photos. We propose an unsupervised EM-style joint inference algorithm with a probabilistic CRF that models identity and role assignments for all detected faces, along with associated pairwise relationships between them. Our experiments illustrate how joint inference of both identity and role (across all photos simultaneously) outperforms independent estimates of each. Joint inference also improves the ability to recognize the same individual across many different photos.
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