Local generic representation for patch uLBP-based face recognition with single training sample per subject

  • Taher Khadhraoui
  • Mohamed Anouar Borgi
  • Faouzi Benzarti
  • Chokri Ben Amar
  • Hamid Amiri


In this paper, we propose a novel paradigm of Patch uniform Local Binary Patterns (PuLBP) based Local Generic Representation (LGR) for face recognition. Indeed, we introduce a new block in which an uLBP is used to approximate both reference and variation subsets. Thus, we concentrate on the challenging problem of a single sample per person in a gallery set. Particularly, the main problem is whether only one training subject per class is available. One of the novelties of our technique is to generate virtual samples of each subject. The new sample generic image in a gallery set is adopted to produce the intra-personal variations of different individuals. We illustrate the experimental results of our new algorithm on different benchmark databases, including the AR face database, the Extended Yale B face database, the FRGC database and the FEI database.


Face recognition Uniform local binary patterns Local generic representation Single training sample per subject 



The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program. D. L acknowledges partial support by NSF DMS 1005799 and DMS 1008900.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Taher Khadhraoui
    • 1
  • Mohamed Anouar Borgi
    • 2
  • Faouzi Benzarti
    • 1
  • Chokri Ben Amar
    • 2
  • Hamid Amiri
    • 1
  1. 1.SITI Laboratory, National Engineering School of Tunis (ENIT)University of Tunis El ManarTunisTunisia
  2. 2.Research Groups on Intelligent MachinesUniversity of SfaxSfaxTunisia

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