Clustering faces in movies using an automatically constructed social network

Clustering faces in movies is a challenging task because faces in a feature-length film are relatively uncontrolled and vary widely in appearance. Such variations make it difficult to appropriately measure the similarity between faces under significantly different settings. In this article, the authors develop a method that improves face-clustering accuracy by incorporating the social context information inherent among characters in a movie. In particular, they study the relation of social network construction and face clustering and present a fusion scheme that eliminates ambiguities and bridges information from two fields. Experiments on real-world data show superior clustering performance compared with state-of-the-art methods. Furthermore, their method can help incrementally build a character's social network that is similar to a manually labeled example.