Machine Vision and Applications

, Volume 26, Issue 6, pp 837–847 | Cite as

Robust face recognition using sparse representation in LDA space

  • Alessandro Adamo
  • Giuliano Grossi
  • Raffaella Lanzarotti
  • Jianyi Lin
Short Paper


In this article, we address the problem of face recognition under uncontrolled conditions. The proposed solution is a numerical robust algorithm dealing with face images automatically registered and projected via the linear discriminant analysis (LDA) into a holistic low-dimensional feature space. At the heart of this discriminative system, there are suitable nonconvex parametric mappings based on which a fixed-point technique finds the sparse representation of test images allowing their classification. We theoretically argue that the success achieved in sparsity promoting is due to the sequence of values imposed on a characteristic parameter of the used mapping family. Experiments carried out on several databases (ORL, YaleB, BANCA, FRGC v2.0) show the robustness and the ability of the system for classification purpose. In particular, within the area of sparsity promotion, our recognition system shows very good performance with respect to those achieved by the state-of-the-art \(\ell _1\) norm-based sparse representation classifier (SRC), the recently proposed \(\ell _2\) norm-based collaborative representation classifier (CRC), the LASSO-based sparse decomposition technique, and the weighted sparse representation method (WSRC), which integrates sparsity and data locality structure.


Sparsity recovery Face recognition Fixed-point iteration schema Nonlinear nonconvex mappings SRC, CRC, LASSO, WSRC algorithms 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Alessandro Adamo
    • 1
  • Giuliano Grossi
    • 2
  • Raffaella Lanzarotti
    • 2
  • Jianyi Lin
    • 2
  1. 1.Department of Mathematics “Federigo Enriques”University of Milan MilanItaly
  2. 2.Department of Computer ScienceUniversity of MilanMilanItaly

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