LRR-TTK DL for face recognition

LRR-TTK DL for face recognition

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Dictionary learning (DL) technique has received a great interest recently, due to its significant role in feature extraction. Although many DL-based methods have been presented, some of them still suffer from the lack of discriminative features, especially for the local manifold features. To mitigate this problem, the authors propose a novel DL method named low-rank representation based on twin tensor kernel (LRR-TTK) DL for face recognition in this study. Specifically, the training samples are projected to a high-dimensional space with TTK. Then, they extract the local manifold features and spatial features (representation coefficients) hidden in the facial images by TT locality preserving projection. In addition, powered by LRR reconstruction and DL theory, much more discriminative features are obtained, which can improve the recognition rate greatly. Comprehensive experimental results at AR, extended Yale-B and FERET face databases demonstrate the superiority of their proposed method.


    1. 1)
      • 1. Lin, T., Liu, S., Zha, H.: ‘Incoherent dictionary learning for sparse representation’. Proc. of the 21st Int. Conf. on Pattern Recognition, 2012, pp. 12371240.
    2. 2)
      • 2. Ma, L., Wang, C., Xiao, B., et al: ‘Sparse representation for face recognition based on discriminative low-rank dictionary learning’. Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 2012, pp. 25862593.
    3. 3)
      • 3. Jiang, Z., Lin, Z., Davis, L.S.: ‘Label consistent K-SVD: learning a discriminative dictionary for recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2013, 35, pp. 26512664.
    4. 4)
      • 4. Yang, M., Zhang, L., Feng, X., et al: ‘Fisher discrimination dictionary learning for sparse representation’. Proc. of the 13th IEEE Int. Conf. on Computer Vision, 2011, pp. 543550.
    5. 5)
      • 5. Yang, J., Wright, J., Huang, T., et al: ‘Image super-resolution as sparse representation of raw image patches’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2008, pp. 18.
    6. 6)
      • 6. Rao, S.R., Tron, R., Vidal, R., et al: ‘Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2008, pp. 18.
    7. 7)
      • 7. Mairal, J., Sapiro, G., Elad, M.: ‘Learning multiscale sparse representations for image and video restoration’, DTIC Document, 2007.
    8. 8)
      • 8. Wright, S.J., Nowak, R.D., Figueiredo, M.A.: ‘Sparse reconstruction by separable approximation’, IEEE Trans. Signal Process., 2009, 57, pp. 24792493.
    9. 9)
      • 9. Li, X., Pang, Y., Yuan, Y.: ‘L1-norm-based 2DPCA’, IEEE Trans. Syst. Man Cybern. B, 2010, 40, pp. 11701175.
    10. 10)
      • 10. Wright, J., Yang, A.Y., Ganesh, A., et al: ‘Robust face recognition via sparse representation’, IEEE Trans. Pattern Anal. Mach. Intell., 2009, 31, pp. 210227.
    11. 11)
      • 11. Yang, M., Zhang, L.: ‘Gabor feature based sparse representation for face recognition with Gabor occlusion dictionary’. Proc. of the Computer Vision, 2010, pp. 448461.
    12. 12)
      • 12. Yang, J., Yu, K., Gong, Y., et al: ‘Linear spatial pyramid matching using sparse coding for image classification’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2009, pp. 17941801.
    13. 13)
      • 13. Wang, J., Yang, J., Yu, K., et al: ‘Locality-constrained linear coding for image classification’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2010, pp. 33603367.
    14. 14)
      • 14. Gao, S., Tsang, I.W., Chia, L.-T., et al: ‘Local features are not lonely–Laplacian sparse coding for image classification’. Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2010, pp. 35553561.
    15. 15)
      • 15. Cui, X., Huang, J., Zhang, S., et al: ‘Background subtraction using low rank and group sparsity constraints computer vision – ECCV 2012’ (Springer, Berlin Heidelberg, 2012), pp. 612625.
    16. 16)
      • 16. Zhang, T., Ghanem, B., Liu, S., et al: ‘Low-rank sparse learning for robust visual tracking computer vision–ECCV 2012’ (Springer, Berlin, Heidelberg, 2012), pp. 470484.
    17. 17)
      • 17. Zhu, G., Yan, S., Ma, Y.: ‘Image tag refinement towards low-rank, content-tag prior and error sparsity’. Proc. of the Int. Conf. on Multimedia, 2010, pp. 461470.
    18. 18)
      • 18. Liu, G., Lin, Z., Yu, Y.: ‘Robust subspace segmentation by low-rank representation’. Proc. of the 27th Int. Conf. on Machine Learning, 2010, pp. 663670.
    19. 19)
      • 19. Liu, G., Lin, Z., Yan, S., et al: ‘Robust recovery of subspace structures by low-rank representation’, ArXiv preprint arXiv:1010.2955, 2010.
    20. 20)
      • 20. Chen, J., Yi, Z.: ‘Sparse representation for face recognition by discriminative low rank matrix recovery’, J. Vis. Commun. Image Represent., 2014, 25, pp. 763773.
    21. 21)
      • 21. Zhang, C., Liu, J., Tian, Q., et al: ‘Image classification by non-negative sparse coding, low-rank and sparse decomposition’. Proc. of the Computer Vision and Pattern Recognition, 2011, pp. 16731680.
    22. 22)
      • 22. Seung, H., Lee, D.: ‘The manifold ways of perception’, Science, 2000, 290, (22), pp. 22682269.
    23. 23)
      • 23. Murase, H., Nayar, S.: ‘Visual learning and recognition of 3D objects from appearance’, Int. J. Comput. Vis., 1995, 14, (1), pp. 524.
    24. 24)
      • 24. Zhang, L., Zhang, L., Tao, D., et al: ‘A multifeature tensor for remote-sensing target recognition’, IEEE Geosci. Remote Sens. Lett., 2011, 8, (2), pp. 374378.
    25. 25)
      • 25. Zhang, L., Zhang, L., Tao, D., et al: ‘Tensor discriminative locality alignment for hyperspectral image spectral-spatial feature extraction’, IEEE Trans. Geosci. Remote Sens., 2013, 51, (1), pp. 242256.
    26. 26)
      • 26. Yang, J., Zhang, D., Frangi, A.F., et al: ‘Two-dimensional PCA: a new approach to appearance-based face representation and recognition’, IEEE Trans. Pattern Anal. Mach. Intell., 2004, 26, (1), pp. 131137.
    27. 27)
      • 27. Liu, Z., Sarkar, S.: ‘Simplest representation yet for gait recognition: averaged silhouette’. IEEE CVPR, 2004, vol. 4, pp. 211214.
    28. 28)
      • 28. He, X., Cai, D., Niyogi, P.: ‘Tensor subspace analysis’. NIPS, 2005, vol. 17, pp. 499506.
    29. 29)
      • 29. Bertsekas, D.P.: ‘Computer science and applied mathematics, constrained optimization and Lagrange multiplier methods, 1’ (Academic Press, Boston, 1982).
    30. 30)
      • 30. Lin, Z., Chen, M., Ma, Y.: ‘The augmented Lagrange multiplier method for exact recovery of corrupted low-rank matrices’, arXiv preprintar Xiv: 1009.5055, 2010.
    31. 31)
      • 31. Beck, A., Teboulle, M.: ‘A fast iterative shrink age-thresholding algorithm for linear inverse problems’, SIAMJ. Imaging Sci., 2009, 2, pp. 183202.
    32. 32)
      • 32. Aharon, M., Elad, M., Bruckstein, A.M.: ‘K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation’, IEEE Trans. Signal Process., 2006, 54, (11), pp. 43114322.
    33. 33)
      • 33. Torki, M., Elgammal, A.: ‘Putting local features on a manifold’. IEEE Conf. on Computer Vision & Pattern Recognition, 2010, pp. 17431750.
    34. 34)
      • 34. Tropp, J.A.: ‘Greed is good: algorithmic results for sparse approximation’, IEEE Trans. Inf. Theory, 2004, 50, (10), pp. 22312242.
    35. 35)
      • 35. Engan, K., Aase, S.O., Husoy, J.H.: ‘Method of optimal directions for frame design’. Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, 1999, pp. 24432446.
    36. 36)
      • 36. Schölkopf, B., Smola, A.: ‘Learning with kernels: support vector machines, regularization, optimization, and beyond’ (The MIT Press, Cambridge, 2002).
    37. 37)
      • 37. Gärtner, T., Lloyd, J.W., Flach, P.A.: ‘Kernels for structured data’. Proc. of the 12th Int. Conf. on Inductive Logic Programming, 2002.
    38. 38)
      • 38. Hardoon, D.R., Shawe-Taylor, J.: ‘Decomposing the tensor kernel support vector machine for neuroscience data with structured labels’, Mach. Learn., 2010, 79, pp. 2946.
    39. 39)
      • 39. Belkin, M., Niyogi, P.: ‘Laplacian eigenmaps and spectral techniques for embedding and clustering’. Advances in Neural Information Processing Systems, Vancouver, British Columbia, Canada, 2002, vol. 14, (6), pp. 585591.
    40. 40)
      • 40. Zhang, L., Yang, M., Feng, X.: ‘Sparse representation or collaborative representation: which helps face recognition?’. Proc. of the IEEE Int. Conf. on Computer Vision, 2011, pp. 471478.

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