Hai-Shun Du, Qing-Pu Hu, Dian-Feng Qiao and Ioannis Pitas. Robust Face Recognition via Low-rank Sparse Representation-based Classification. International Journal of Automation and Computing, vol. 12, no. 6, pp. 579-587, 2015. https://doi.org/10.1007/s11633-015-0901-2
Citation: Hai-Shun Du, Qing-Pu Hu, Dian-Feng Qiao and Ioannis Pitas. Robust Face Recognition via Low-rank Sparse Representation-based Classification. International Journal of Automation and Computing, vol. 12, no. 6, pp. 579-587, 2015. https://doi.org/10.1007/s11633-015-0901-2

Robust Face Recognition via Low-rank Sparse Representation-based Classification

doi: 10.1007/s11633-015-0901-2
Funds:

This work was supported by National Natural Science Foundation of China (No. 61374134) and the key Scientific Research Project of Universities in Henan Province, China (No. 15A413009).

  • Received Date: 2015-10-24
  • Rev Recd Date: 2015-03-04
  • Publish Date: 2015-12-01
  • Face recognition has attracted great interest due to its importance in many real-world applications. In this paper, we present a novel low-rank sparse representation-based classification (LRSRC) method for robust face recognition. Given a set of test samples, LRSRC seeks the lowest-rank and sparsest representation matrix over all training samples. Since low-rank model can reveal the subspace structures of data while sparsity helps to recognize the data class, the obtained test sample representations are both representative and discriminative. Using the representation vector of a test sample, LRSRC classifies the test sample into the class which generates minimal reconstruction error. Experimental results on several face image databases show the effectiveness and robustness of LRSRC in face image recognition.

     

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