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Similarity Searches in Face Databases

  • Annalisa Franco
  • Dario Maio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5716)

Abstract

In this paper the problem of similarity searches in face databases is addressed. An approach based on relevance feedback is proposed to iteratively improve the query result. The approach is suitable both to supervised and unsupervised contexts. The efficacy of the learning procedures are confirmed by the results obtained on publicly available databases of faces.

Keywords

Similarity Search Local Binary Pattern Query Image Relevance Feedback Face Database 
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 2009

Authors and Affiliations

  • Annalisa Franco
    • 1
  • Dario Maio
    • 1
  1. 1.DEISUniversità di BolognaBolognaItaly

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