Volume 12 Number 6
December 2015
Article Contents
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. doi: 10.1007/s11633-015-0901-2
Cite as: 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. doi: 10.1007/s11633-015-0901-2

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

  • Received: 2015-10-24
Fund Project:

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).

  • 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.
  • [1] J. B. Tenenbaum, V. de Silva, J. C. Langford. A global geometric framework for nonlinear dimensionality reduction. Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
    [2] S. T. Roweis, L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding. Science, vol. 290, no. 5500, pp. 2323-2326, 2000.
    [3] M. A. Turk, A. P. Pentland. Face recognition using eigenfaces. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, Maui, USA, pp. 586-591, 1991.
    [4] P. N. Belhumeur, J. P. Hespanha, D. J. kriegman. Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711-720, 1997.
    [5] X. F. He, S. C. Yan, Y. X. Hu, P. Niyogi, H. J. Zhang. Face recognition using laplacianfaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 3, pp. 328-340, 2005.
    [6] S. C. Yan, D. Xu, B. Y. Zhang, H. J. Zhang, Q. Yang, S. Lin. Graph embedding and extensions: A general framework for dimensionality reduction. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, 2007.
    [7] H. T. Chen, H. W. Chang, T. L. Liu. Local discriminant embedding and its variants. In Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, San Diego, USA, pp. 846-853, 2005.
    [8] G. Q.Wang, L. X. Li, X. B. Guo. Sparsity preserving discriminant embedding for face recognition. Chinese Journal of Scientific Instrument, vol. 35, no. 2, pp. 305-312, 2014. (in Chinese)
    [9] R. O. Duda, P. E. Hart, D.G. Stork. Pattern Classification, 2nd ed., New York, USA: John Wiley & Sons, 2001.
    [10] F. De la Torre, M. J. Black. Robust principal component analysis for computer vision. In Proceedings of the 8th IEEE International Conference on Computer Vision, IEEE, Vancouver, USA, pp. 362-369, 2001.
    [11] Z. Z. Feng, M. Yang, L. Zhang, Y. Liu, D. Zhang. Joint discriminative dimensionality reduction and dictionary learning for face recognition. Pattern Recognition, vol. 46, no. 8, pp. 2134-2143, 2013.
    [12] J. Wright, A. Y. Yang, A. Ganesh, S. Sastry, Y. Ma. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, 2009.
    [13] S. S. Chen, D. L. Donoho, M. A. Saunders. Atomic decomposition by basis pursuit. SIAM Review, vol. 43, no. 1, pp. 129-159, 2001.
    [14] S. J. Kim, K. Koh, M. Lustig, S. Boyd, D. Gorinevsky. An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing, vol. 1, no. 4, pp. 606-617, 2007.
    [15] J. F. Yang, Y. Zhang. Alternating direction algorithm for l1-problems in compressive sensing. SIAM Journal on Scientific Computing, vol. 33, no. 1, pp. 250-278, 2011.
    [16] W. H. Deng, J. N. Hu, J. Guo. Extended SRC: Undersampled face recognition via intraclass variant dictionary. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, no. 9, pp. 1864-1870, 2012.
    [17] L. Zhang, M. Yang, X. Feng. Sparse representation or collaborative representation: which helps face recognition: which helps face recognition? In Proceedings of International Conference on Computer Vision, Barcelona, Spain, pp. 471-478, 2011.
    [18] I. Naseem, R. Togneri, M. Bennamoun. Linear regression for face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 11, pp. 2106-2112, 2010.
    [19] G. C. Liu, Z. C. Lin, S. C. Yan, Y. Yu, Y. Ma. Robust recovery of subspace structures by low-rank representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 1, pp. 171-184, 2013.
    [20] X. Q. Lu, Y. L.Wang, Y. Yuan. Graph-regularized low-rank representation for destriping of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, vol. 51, no. 7, pp. 4009-4018, 2013.
    [21] L. Ma, C. H. Wang, B. H. Xiao, W. Zhou. Sparse representation for face recognition based on discriminative low-rank dictionary learning. In Proceedings of International Conference on Computer Vision and Pattern Recognition, IEEE, Providence, USA, pp. 2586-2593, 2012.
    [22] Y. M. Z. Zhang, Z. L. Jiang, L. S. Davis. Learning structured low-rank representation for image classification. In Proceedings of International Conference on Computer Vision and Pattern Recognition, IEEE, Portland, USA, pp. 676-683, 2013.
    [23] D. P. Bertsekas. Constrained Optimization and Lagrange Multiplier Methods, New York, USA: Academic Press, 1982.
    [24] Z. C. Lin, R. S. Liu, Z. X. Su. Linearized alternating direction method with adaptive penalty for low-rank representation. In Proceedings of Neural Information Processing Systems, Granada, Spain, pp. 1-9 2011.
    [25] L. S. Zhuang, H. Y. Gao, Z. C. Lin, Y. Ma, X. Zhang, N. H. Yu. Non-negative low rank and sparse graph for semisupervised learning. In Proceedings of International Conference on Computer Vision and Pattern Recognition, IEEE, Providence, USA, pp. 2328-2335, 2012.
    [26] N. Zhang, J. Yang. Low-rank representation based discriminative projection for robust feature extraction. Neurocomputing, vol. 111, pp. 13-20, 2013.
    [27] J. F. Cai, E. J. Canèds, Z. W. Shen. A singular value thresholding algorithm for matrix completion. SIAM Journal on Optimization, vol. 20, no. 4, pp. 1956-1982, 2010.
    [28] Z. C. Lin, M. M. Chen, Y. Ma. The augmented Lagrange multiplier method for exact recovery of corrupted lowrank matrices, UIUC Technical Report, UILU-ENG-09-2215, University of Illinois at Urbana-Champaign, USA, 2009.
    [29] A. S. Georghiades, P. N. Belhumeur, D. J. Kriegman. From few to many: Illumination cone models for face recognition under variable lighting and pose. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 643-660, 2001.
    [30] T. Sim, S. Baker, M. Bsat. The CMU pose, illumination, and expression database. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1615-1618, 2003.
    [31] A. M. Martinez, R. Benavente. The AR face database, CVC Technical Report 24, Purdue University, USA, 1998.
    [32] C. F. Chen, C. P. Wei, Y. C. F. Wang. Low-rank matrix recovery with structural incoherence for robust face recognition. In Proceedings of International Conference on Computer Vision and Pattern Recognition, IEEE, Providence, USA, pp. 2618-2625, 2012.
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Robust Face Recognition via Low-rank Sparse Representation-based Classification

Fund Project:

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).

Abstract: 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.

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. doi: 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. doi: 10.1007/s11633-015-0901-2
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