Single Sample Face Recognition by Sparse Recovery of Deep-Learned LDA Features

  • Matteo Bodini
  • Alessandro D’Amelio
  • Giuliano Grossi
  • Raffaella LanzarottiEmail author
  • Jianyi Lin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11182)


Single Sample Per Person (SSPP) Face Recognition is receiving a significant attention due to the challenges it opens especially when conceived for real applications under unconstrained environments. In this paper we propose a solution combining the effectiveness of deep convolutional neural networks (DCNN) feature characterization, the discriminative capability of linear discriminant analysis (LDA), and the efficacy of a sparsity based classifier built on the \(k\)-LiMapS algorithm. Experiments on the public LFW dataset prove the method robustness to solve the SSPP problem, outperforming several state-of-the-art methods.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Matteo Bodini
    • 1
  • Alessandro D’Amelio
    • 1
  • Giuliano Grossi
    • 1
  • Raffaella Lanzarotti
    • 1
    Email author
  • Jianyi Lin
    • 2
  1. 1.PHuSe Lab - Dipartimento di InformaticaUniversità degli Studi di MilanoMilanoItaly
  2. 2.Department of MathematicsKhalifa University of Science and TechnologyAbu DhabiUnited Arab Emirates

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