A Selection Module for Large-Scale Face Recognition Systems

  • Giuliano Grossi
  • Raffaella LanzarottiEmail author
  • Jianyi Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9280)


Face recognition systems aimed at working on large scale datasets are required to solve specific hurdles. In particular, due to the huge amount of data, it becomes mandatory to furnish a very fast and effective approach. Moreover the solution should be scalable, that is it should deal efficiently the growing of the gallery with new subjects. In literature, most of the works tackling this problem are composed of two stages, namely the selection and the classification. The former is aimed at significantly pruning the face image gallery, while the latter, often expensive but precise, determines the probe identity on this reduced domain. In this article a new selection method is presented, combining a multi-feature representation and the least squares method. Data are split into sub-galleries so as to make the system more efficient and scalable. Experiments on the union of four challenging datasets and comparisons with the state-of-the-art prove the effectiveness of our method.


Partial Little Square Face Recognition Face Image Sparse Representation Local Binary Pattern 
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 International Publishing Switzerland 2015

Authors and Affiliations

  • Giuliano Grossi
    • 1
  • Raffaella Lanzarotti
    • 1
    Email author
  • Jianyi Lin
    • 1
  1. 1.Dipartimento di InformaticaUniversità degli Studi di MilanoMilanoItaly

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