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Context-Aided Human Recognition – Clustering

  • Yang Song
  • Thomas Leung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3953)

Abstract

Context information other than faces, such as clothes, picture-taken-time and some logical constraints, can provide rich cues for recognizing people. This aim of this work is to automatically cluster pictures according to person’s identity by exploiting as much context information as possible in addition to faces. Toward that end, a clothes recognition algorithm is first developed, which is effective for different types of clothes (smooth or highly textured). Clothes recognition results are integrated with face recognition to provide similarity measurements for clustering. Picture-taken-time is used when combining faces and clothes, and the cases of faces or clothes missing are handled in a principle way. A spectral clustering algorithm which can enforce hard constraints (positive and negative) is presented to incorporate logic-based cues (e.g. two persons in one picture must be different individuals) and user feedback. Experiments on real consumer photos show the effectiveness of the algorithm.

Keywords

Face Recognition Context Information Face Detection Spectral Cluster Color Histogram 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yang Song
    • 1
  • Thomas Leung
    • 1
  1. 1.Fujifilm Software (California), Inc.San JoseUSA

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