Virtual Samples Construction Using Image-Block-Stretching for Face Recognition

  • Yingnan ZhaoEmail author
  • Xiangjian He
  • Beijing Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9877)


Face recognition encounters the problem that multiple samples of the same object may be very different owing to the deformation of appearances. To synthesizing reasonable virtual samples is a good way to solve it. In this paper, we introduce the idea of image-block-stretching to generate virtual images for deformable faces. It allows the neighbored image blocks to be stretching randomly to reflect possible variations of the appearance of faces. We demonstrate that virtual images obtained using image-block-stretching and original images are complementary in representing faces. Extensive classification experiments on face databases show that the proposed virtual image scheme is very competent and can be combined with a number of classifiers, such as the sparse representation classification, to achieve surprising accuracy improvement.


Face recognition Virtual image Sparse representation 



This work is supported in part by the PAPD of Jiangsu Higher Education Institutions, Natural Science Foundation of China (No. 61572258, No. 61103141 and No. 51505234), and the Natural Science Foundation of Jiangsu Province (No. BK20151530).


  1. 1.
    Zhang, L., Chen, S., Qiao, L.: Graph optimization for dimensionality reduction with sparsity constraints. Pattern Recogn. 45(3), 1205–1210 (2012)CrossRefzbMATHGoogle Scholar
  2. 2.
    Fan, Z., Xu, Y., Zhang, D.: Local linear discriminant analysis framework using sample neighbors. IEEE Trans. Neural Netw. 22(7), 1119–1132 (2011)CrossRefGoogle Scholar
  3. 3.
    Wang, S.J., Yang, J., Sun, M.-F., et al.: Sparse tensor discriminant color space for face verification. IEEE Trans. Neural Netw. Learn. Syst. 23(6), 876–888 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, D., Song, F., Xu, Y., et al.: Advanced pattern recognition technologies with applications to biometrics. Medical Information Science Reference, New York (2009)CrossRefGoogle Scholar
  5. 5.
    Zhang, X., Gao, Y.: Face recognition across pose: a review. Pattern Recogn. 42(11), 2876–2896 (2009)CrossRefGoogle Scholar
  6. 6.
    Kautkar, S.N., Atkinson, G.A., Smith, M.L.: Face recognition in 2D and 2.5D using ridgelets and photometric stereo. Pattern Recogn. 45(9), 3317–3327 (2012)CrossRefGoogle Scholar
  7. 7.
    Shahrokni, A., Fleuret, F., Fua, P.: Classifier-based contour tracking for rigid and deformable objects. In: British Machine Vision Conference, No. CVLAB-CONF-2005-014 (2005)Google Scholar
  8. 8.
    Zhang, P., You, X., Ou, W., et al.: Sparse discriminative multi-manifold embedding for one-sample face identification. Pattern Recogn. 52, 249–259 (2016)CrossRefGoogle Scholar
  9. 9.
    Sun, Z.L., Shang, L.: A local spectral feature based face recognition approach for the one-sample-per-person problem. Neurocomputing 188, 160–166 (2016)CrossRefGoogle Scholar
  10. 10.
    Tang, B., Luo, S., Huang, H.: High performance face recognition system by creating virtual sample. In: Proceedings of the International Conference on Neural Networks and Signal Processing, pp. 972–975 (2003)Google Scholar
  11. 11.
    Thian, N.P.H., Marcel, S., Bengio, S.: Improving face authentication using virtual samples. In: Proceeding of the IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 6–10 (2003)Google Scholar
  12. 12.
    Ryu, Y.-S., Oh, S.-Y.: Simple hybrid classifier for face recognition with adaptively generated virtual data. Pattern Recogn. Lett. 23(7), 833–841 (2002)CrossRefzbMATHGoogle Scholar
  13. 13.
    Beymer, D., Poggio, T.: Face recognition from one example view. In: Proceedings of the Fifth International Conference on Computer Vision, pp. 500–507 (1995)Google Scholar
  14. 14.
    Vetter, T.: Synthesis of novel views from a single face image. Int. J. Comput. Vis. 28(2), 102–116 (1998)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Jung, H.-C.: Authenticating corrupted face image based on noise model. In: Proceedings of the Sixth IEEE International Conference on Automatic Face and Gesture Recognition, pp. 272–277 (2004)Google Scholar
  16. 16.
    Sharma, A., Dubey, A., Tripathi, P., et al.: Pose invariant virtual classifiers from single training image using novel hybrid-eigenfaces. Neurocomputing 73(10–12), 1868–1880 (2010)CrossRefGoogle Scholar
  17. 17.
    Liu, J., Chen, S., Zhou, Z.-H., Tan, X.: Single image subspace for face recognition. In: Zhou, S., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 205–219. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  18. 18.
    Xu, Y., Zhu, X., Li, Z., et al.: Using the original and ‘symmetrical face’ training samples to perform representation based two-step face recognition. Pattern Recogn. 46(4), 1151–1158 (2013)CrossRefGoogle Scholar
  19. 19.
    Xu, Y., Li, X., Yang, J.: Integrate the original face image and its mirror image for face recognition. Neurocomputing 131, 191–199 (2014)CrossRefGoogle Scholar
  20. 20.
    Xu, Y., Fagn, X., Li, X., et al.: Data uncertainty in face recognition. IEEE Trans. Cybern. 44(10), 1950–1961 (2014)CrossRefGoogle Scholar
  21. 21.
    Nguyen, H.T., Ong, E.P., Niswar, A., et al.: Automatic and real-time 3D face synthesis. In: VRCAI, pp. 103–106 (2009)Google Scholar
  22. 22.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefGoogle Scholar
  23. 23.
    Zhang, D., Yang, M., Feng, X.-Ch: Sparse representation or collaborative representation: Which helps face recognition? In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 471–478 (2011)Google Scholar
  24. 24.
    Meng, Y., Zhang, D., Wang, S.: Relaxed collaborative representation for pattern classification. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2224–2231 (2012)Google Scholar
  25. 25.
    Imran, N., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)CrossRefGoogle Scholar
  26. 26.
    Zhang, Z., Xu, Y., Yang, J., et al.: A survey of sparse representation: algorithms and applications. IEEE Access 3, 490–530 (2015)CrossRefGoogle Scholar
  27. 27.
    Xu, Y., Lu, Y.: Adaptive weighted fusion: A novel fusion approach for image classification. Neurocomputing 168, 566–574 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Computer & SoftwareNanjing University of Information Science and TechnologyNanjingChina
  3. 3.School of Computing and CommunicationsUniversity of Technology SydneySydneyAustralia

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