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Identity and Kinship Relations in Group Pictures

Abstract

This chapter studies the problem of identifying people in group pictures. That is, determining from a gallery of people who appear in a given picture. This is a well-studied problem that is becoming increasingly important given the recent explosion in usage of social networks. In this chapter we make two distinct contributions to this problem. First, we use novel kinship similarity to make better estimation of identity. Specifically, we use unary costs based on state-of-the-art face recognition algorithms and as pairwise cost we use the kinship similarity of the people in the image. Second, with these values we formulate a collection-specific MRF MAP estimation (labelling) problem and use existing MRF MAP estimation methods to solve it. To evaluate the proposed method, a family photo database is collected from the Internet. Experiments show that for group pictures of family members (family pictures) our method obtains the state-of-the-art performance, while performing competitively in nonfamily group pictures.

Keywords

  • Support Vector Machine
  • Face Recognition
  • Kinship Similarity
  • Stereo Match
  • Kinship Relationship

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Ming Shao .

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Shao, M., Xia, S., Fu, Y. (2014). Identity and Kinship Relations in Group Pictures. In: Fu, Y. (eds) Human-Centered Social Media Analytics. Springer, Cham. https://doi.org/10.1007/978-3-319-05491-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-05491-9_9

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