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Volume 5 Issue 2
Mar.  2018

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2018  >  5(2): 547-554
Jianquan Gu, Haifeng Hu and Haoxi Li, "Local Robust Sparse Representation for Face Recognition With Single Sample per Person," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 547-554, Mar. 2018. doi: 10.1109/JAS.2017.7510658
Citation: Jianquan Gu, Haifeng Hu and Haoxi Li, "Local Robust Sparse Representation for Face Recognition With Single Sample per Person," IEEE/CAA J. Autom. Sinica, vol. 5, no. 2, pp. 547-554, Mar. 2018. doi: 10.1109/JAS.2017.7510658
shu

Local Robust Sparse Representation for Face Recognition With Single Sample per Person

doi: 10.1109/JAS.2017.7510658
  • 1.

    School of Electronics and Information Technology, Sun Yat-sen University(SYSU), Guangzhou 510275, China

  • 2.

    SYSU-CMU Shunde International Joint Research Institute, Shunde 528300, China

Funds:

the National Natural Science Foundation of China 61673402

the National Natural Science Foundation of China 61273270

the National Natural Science Foundation of China 60802069

the Natural Science Foundation of Guangdong Province 2017A030311029

the Natural Science Foundation of Guangdong Province 2016B010109002

the Natural Science Foundation of Guangdong Province 2015B090912001

the Natural Science Foundation of Guangdong Province 2016B010123005

the Natural Science Foundation of Guangdong Province 2017B090909005

the Science and Technology Program of Guangzhou of China 201704020180

the Science and Technology Program of Guangzhou of China 201604020024

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    Abstract
  • The purpose of this paper is to solve the problem of robust face recognition (FR) with single sample per person (SSPP). In the scenario of FR with SSPP, we present a novel model local robust sparse representation (LRSR) to tackle the problem of query images with various intra-class variations, e.g., expressions, illuminations, and occlusion. FR with SSPP is a very difficult challenge due to lacking of information to predict the possible intra-class variation of the query images. The key idea of the proposed method is to combine a local sparse representation model and a patch-based generic variation dictionary learning model to predict the possible facial intra-class variation of the query images. The experimental results on the AR database, Extended Yale B database, CMU-PIE database and LFW database show that the proposed method is robust to intra-class variations in FR with SSPP, and outperforms the state-of-art approaches.

     

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    • References(30)
  • [1]
    H. T. Zhao and P. C. Yuen, "Incremental linear discriminant analysis for face recognition, " IEEE Trans. Syst. Man Cybernet. B, vol. 38, no. 1, pp. 210-221, Feb. 2008. http://ieeexplore.ieee.org/document/4400726/
    [2]
    X. Tan, S. Chen, Z. -H. Zhou, and F. Zhang, "Face recognition from a single image per person: A survey, " Pattern Recognit. , vol. 39, no. 9, pp. 1725-1745, Sep. 2006. http://www.sciencedirect.com/science/article/pii/S0031320306001270
    [3]
    S. Z. Li and J. W. Lu, "Face recognition using the nearest feature line method, " IEEE Trans. Neural Netw. , vol. 10, no. 2, pp. 439-443, Mar. 1999. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=750575
    [4]
    M. Turk and A. Pentland, "Eigenfaces for recognition, " J. Cognit. Neurosci., vol.3, no.1, pp.71-86, 1991. doi: 10.1162/jocn.1991.3.1.71
    [5]
    S. C. Chen, J. Liu, and Z. H. Zhou, "Making FLDA applicable to face recognition with one sample per person, " Pattern Recognit. , vol. 37, no. 7, pp. 1553-1555, Jul. 2004. http://www.sciencedirect.com/science/article/pii/S0031320304000123
    [6]
    J. W. Lu, Y. P. Tan, and G. Wang, "Discriminative multimanifold analysis for face recognition from a single training sample per person, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 35, no. 1, pp. 39-51, Jan. 2013. http://ieeexplore.ieee.org/document/6175025/
    [7]
    R. Kumar, A. Banerjee, B. C. Vemuri, and H. Pfister, "Maximizing all margins: Pushing face recognition with kernel plurality, " in Proc. 2011 IEEE Int. Conf. Computer Vision (ICCV), Barcelona, Spain, pp. 2375-2382, 2011. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6126520
    [8]
    P. F. Zhu, L. Zhang, Q. H. Hu, and S. C. K. Shiu, "Multi-scale patch based collaborative representation for face recognition with margin distribution optimization, " in Proc. 12th European Conf. Computer Vision-ECCV 2012, Berlin Heidelberg, Germany, pp. 822-835, 2012. doi: 10.1007/978-3-642-33718-5_59
    [9]
    T. Ahonen, A. Hadid, and M. Pietikainen, "Face description with local binary patterns: Application to face recognition, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 28, no. 12, pp. 2037-2041, Dec. 2006. http://ieeexplore.ieee.org/document/1717463/
    [10]
    J. Zou, Q. Ji, and G. Nagy, "A comparative study of local matching approach for face recognition, " IEEE Trans. Image Process. , vol. 16, no. 10, pp. 2617-2628, Oct. 2007. http://ieeexplore.ieee.org/document/4303157/
    [11]
    L. Zhang, M. Yang, and X. C. Feng, "Sparse representation or collaborative representation: Which helps face recognition, " in Proc. 2011 IEEE Int. Conf. Computer Vision (ICCV), Barcelona, Spain, pp. 471-478, 2011. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6126277
    [12]
    P. F. Zhu, M. Yang, L. Zhang, and I. Y. Lee, "Local generic representation for face recognition with single sample per person, " in Proc. Asian Conf. Computer Vision Computer on Vision-ACCV 2014, Switzerland, pp. 34-50, 2014. doi: 10.1007%2F978-3-319-16811-1_3
    [13]
    W. H. Deng, J. N. Hu, and J. Guo, "Extended SRC: Undersampled face recognition via intraclass variant dictionary, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 34, no. 9, pp. 1864-1870, Sep. 2012. Extended SRC: Undersampled face recognition via intraclass variant dictionary
    [14]
    M. Yang, L. Van Gool, and L. Zhang, "Sparse variation dictionary learning for face recognition with a single training sample per person, " in Proc. 2013 IEEE Int. Conf. Computer Vision, Sydney, Australia, pp. 689-696, 2013. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=6751195
    [15]
    Y. Su, S. G. Shan, X. L. Chen, and W. Gao, "Adaptive generic learning for face recognition from a single sample per person, " in Proc. 2010 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), San Francisco, USA, pp. 2699-2706, 2010. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5539990
    [16]
    M. N. Kan, S. G. Shan, Y. Su, D. Xu, and X. L. Chen, "Adaptive discriminant learning for face recognition, " Pattern Recognit. , vol. 46, no. 9, pp. 2497-2509, Sep. 2013. http://www.sciencedirect.com/science/article/pii/S0031320313000769
    [17]
    M. Yang, L. Zhang, J. Yang, and D. Zhang, "Robust sparse coding for face recognition, " in Proc. 2011 IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Providence, RI, USA, pp. 625-632, 2011. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=5995393
    [18]
    L. Y. Fang and S. T. Li, "Face recognition by exploiting local Gabor features with multitask adaptive sparse representation, " IEEE Trans. Instrum. Measur., vol. 64, no. 10, pp. 2605-2615, Oct. 2015. http://ieeexplore.ieee.org/document/7106485/
    [19]
    Z. M. Li, Z. H. Huang, and K. Shang, "A customized sparse representation model with mixed norm for undersampled face recognition, " IEEE Trans. Inform. Forens. Secur. , vol. 11, no. 10, pp. 2203-2214, Oct. 2016. http://ieeexplore.ieee.org/document/7468460/
    [20]
    R. He, W. S. Zheng, T. N. Tan, and Z. N. Sun, "Half-quadratic-based iterative minimization for robust sparse representation, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 36, no. 2, pp. 261-275, Feb. 2014. http://ieeexplore.ieee.org/document/6518114/
    [21]
    R. He, W. S. Zheng, and B. G. Hu, "Maximum correntropy criterion for robust face recognition, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, no. 8, pp. 1561-1576, Aug. 2011. http://ieeexplore.ieee.org/document/5661789/
    [22]
    R. He, W. S. Zheng, B. G. Hu, and X. W. Kong, "Two-stage nonnegative sparse representation for large-scale face recognition, " IEEE Trans. Neural Netw. Learn. Syst., vol. 24, no. 1, pp. 35-46, Jan. 2013. http://ieeexplore.ieee.org/document/6365317/
    [23]
    J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation, " IEEE Trans. Pattern Anal. Mach. Intell. , vol. 31, no. 2, pp. 210-227, Feb. 2009. http://ieeexplore.ieee.org/document/4483511
    [24]
    R. Rubinstein, A. M. Bruckstein, and M. Elad, "Dictionaries for sparse representation modeling, " Proc. IEEE, vol. 98, no. 6, pp. 1045-1057, Jun. 2010. http://ieeexplore.ieee.org/document/5452966/
    [25]
    A. Martínez and R. Benavente, "The AR face database, " Centre de Visió per Comp., Univ. Autónoma de Barcelona, Spain, CVC Tech. Rep. #24, Jun. 1998.
    [26]
    A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, "From few to many: generative models for recognition under variable pose and illumination, " in Proc. 4th IEEE Int. Conf. Automatic Face and Gesture Recognition, Grenoble, France, pp. 277-284, 2000. http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=840647
    [27]
    T. Sim, S. Baker, and M. Bsat, "The CMU pose, illumination, and expression (PIE) database, " in Proc. 5th IEEE Int. Conf. Automatic Face and Gesture Recognition, Washington, DC, USA, pp. 46-51, 2002. http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=1004130
    [28]
    G. B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller, "Labeled faces in the wild: a database for studying face recognition in unconstrained environments, " Univ. Massachusetts, USA, Tech. Rep. 07-49, 2007.
    [29]
    R. Gross, I. Matthews, J. Cohn, T. Kanade, and S. Baker, "Multi-pie, " Image Vision Comp., vol. 28, no. 5, pp. 807-813, May 2010.
    [30]
    T. Hassner, S. Harel, E. Paz, and R. Enbar, "Effective face frontalization in unconstrained images, " in Proc. 2015 IEEE Conf. Computer Vision and Pattern Recognition, Boston, MA, USA, pp. 4295-4304, 2015. http://adsabs.harvard.edu/abs/2014arXiv1411.7964H%201
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