Automatic Control and Computer Sciences

, Volume 53, Issue 6, pp 550–559 | Cite as

Cropped and Extended Patch Collaborative Representation Face Recognition for a Single Sample Per Person

  • Huixian YangEmail author
  • Weifa Gan
  • Fan Chen
  • Jinfang Zeng


Face recognition for a single sample per person (SSPP) is a challenging task due to the lack of sufficient sample information. In this paper, in order to raise the performance of face recognition for SSPP, we propose an algorithm of cropped and extended patch collaborative representation for a single sample per person (CEPCRC). Considering the fact that patch-based method can availably avoid the effect of variations, and the fact that intra-class variations learned from a generic training set can sparsely represent the possible facial variations, thus, we extend patch collaborative representation based classification into the SSPP scenarios by using the intra-class variant dictionary and learn patch weight by exploiting regularized margin distribution optimization. For more complementary information, we construct multiple training samples by the means of cropping. Experimental results in the CMU PIE, Extended Yale B, AR, and LFW datasets demonstrate that CEPCRC performs better compared to the related algorithms.


face recognition single sample patch-based method collaborative representation based classification margin distribution optimization 



This work was supported by Hunan Natural Science Foundation, grant no. 2018JJ3486.


The authors declare that they have no conflicts of interest.


  1. 1.
    Zhang, J., Yan, Y., and Lades, M., Face recognition: Eigenface, elastic matching, and neural nets, Proc. IEEE, 1997, vol. 85, no. 9, pp. 1423–1435.CrossRefGoogle Scholar
  2. 2.
    Murthy, K.R. and Ghosh, A., Norm discriminant eigenspace transform for pattern classification, IEEE Trans. Cybern., 2019, vol. 49, no. 1, pp. 273–286.CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., and Kriegman, D.J., Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection, IEEE Trans. Pattern Anal. Mach. Intell., 2002, vol. 19, no. 7, pp. 711–720.CrossRefGoogle Scholar
  4. 4.
    Ye, H., Li, Y., Chen, C., et al., Fast fisher discriminant analysis with randomized algorithms, Pattern Recognit., 2017, vol. 72, pp. 82–92.CrossRefGoogle Scholar
  5. 5.
    Wright, J., Ganesh, A., Zhou, Z., et al., Demo: Robust face recognition via sparse representation, IEEE International Conference on Automatic Face and Gesture Recognition, 2009, vol. 31, no. 2, pp. 210–227.Google Scholar
  6. 6.
    Su, Y., Liu, Z., and Wang, M., Sparse representation based face recognition against expression and illumination, IET Image Processing, 2018, vol. 12, no. 5, pp. 826–832.CrossRefGoogle Scholar
  7. 7.
    He, K., Zhang, X., Ren, S., et al., Deep residual learning for image recognition, IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778.Google Scholar
  8. 8.
    Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 [cs.CV], 2014.Google Scholar
  9. 9.
    Krizhevsky, A., Sutskever, I., and Hinton, G.E., ImageNet classification with deep convolutional neural networks, International Conference on Neural Information Processing Systems, 2012, pp. 1097–1105.Google Scholar
  10. 10.
    Srivastava, R.K., Greff, K., and Schmidhuber, J., Training very deep networks, arXiv:1507.06228 [cs.LG], 2015.Google Scholar
  11. 11.
    Mohammadzade, H. and Hatzinakos, D., Projection into expression subspaces for face recognition from single sample per person, IEEE Trans. Affective Comput., 2013, vol. 4, no. 1, pp. 69–82.CrossRefGoogle Scholar
  12. 12.
    Pan, J., Wang, X.S., and Cheng, Y.H., Single-sample face recognition based on LPP feature transfer, IEEE Access, 2016, vol. 4, pp. 2873–2884.CrossRefGoogle Scholar
  13. 13.
    Shan, S., Cao, B., Gao, W., et al., Extended Fisherface for face recognition from a single example image per person, IEEE International Symposium on Circuits and Systems, 2008, vol. 2.Google Scholar
  14. 14.
    Yan, H., Lu, J., Zhou, X., et al., Multi-feature multi-manifold learning for single-sample face recognition, Neurocomputing, 2014, vol. 143, no. 16, pp. 134–143.CrossRefGoogle Scholar
  15. 15.
    Li, W. and Liang, J., Adaptive face representation via class-specific and intra-class variation dictionaries for recognition, Multimedia Tools Appl., 2017, no. 11, pp. 1–20.Google Scholar
  16. 16.
    Su, Y., Shan, S., Chen, X., et al., Adaptive generic learning for face recognition from a single sample per person, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010.Google Scholar
  17. 17.
    Deng, W., Hu, J., and Guo, J., Extended SRC: Undersampled face recognition via intraclass variant dictionary, IEEE Trans. Pattern Anal. Mach. Intell., 2012, vol. 34, no. 9, pp. 1864–1870.CrossRefGoogle Scholar
  18. 18.
    Wei, C.P. and Wang, Y.C.F., Undersampled face recognition with one-pass dictionary learning, IEEE International Conference on Multimedia and Expo, 2015, pp. 1–6.Google Scholar
  19. 19.
    Yu, Y.F., Dai, D.Q., Ren, C.X., et al., Discriminative multi-scale sparse coding for single-sample face recognition with occlusion, Pattern Recognit., 2017, vol. 66, pp. 302–312.CrossRefGoogle Scholar
  20. 20.
    Zhu, P., Yang, M., Zhang, L., et al., Local generic representation for face recognition with single sample per person, Lect. Notes Comput. Sci., 2014, vol. 9005.Google Scholar
  21. 21.
    Khadhraoui, T., Borgi, M.A., Benzarti, F., et al., Local generic representation for patch uLBP-based face recognition with single training sample per subject, Multimedia Tools Appl., 2018, no. 12, pp. 1–20.Google Scholar
  22. 22.
    Zhang Lei, Meng Yang, and Xiangchu Feng, Sparse representation or collaborative representation: Which helps face recognition?, 2011 IEEE International Conference on Computer Vision, 2011.Google Scholar
  23. 23.
    Yang, M., Wang, X., Zeng, G., et al., Joint and collaborative representation with local adaptive convolution feature for face recognition with single sample per person, Pattern Recognit., 2016, vol. 66, pp. 117–128.CrossRefGoogle Scholar
  24. 24.
    Lu, J., Tan, Y.P., and Wang, G., Discriminative multi-manifold analysis for face recognition from a single training sample per person, IEEE Trans. Pattern Anal. Mach. Intell., 2012, vol. 35, no. 1, pp. 39–51.CrossRefGoogle Scholar
  25. 25.
    Gao, S., Jia, K., Zhuang, L., et al., Neither global nor local: Regularized patch-based representation for single sample per person face recognition, Int. J. Comput. Vision, 2015, vol. 111, no. 3, pp. 365–383.MathSciNetCrossRefGoogle Scholar
  26. 26.
    Lu, J., Tan, Y.P., and Wang, G., Discriminative multimanifold analysis for face recognition from a single training sample per person, IEEE Trans. Pattern Anal. Mach. Intell., 2012, vol. 35, no. 1, pp. 39–51.CrossRefGoogle Scholar
  27. 27.
    Gu, J., Liu, L., and Hu, H., Patch-based sparse dictionary representation for face recognition with single sample per person, Lect. Notes Comput. Sci., 2015, vol. 9428, pp. 120–126.CrossRefGoogle Scholar
  28. 28.
    Zhu, P., Zhang, L., Hu, Q., et al., Multi-scale patch based collaborative representation for face recognition with margin distribution optimization, European Conference on Computer Vision, 2012, pp. 822–835.Google Scholar
  29. 29.
    Rosset, S., Zhu, J., and Hastie, T., Boosting as a regularized path to a maximum margin classifier, J. Mach. Learn. Res., 2004, vol. 5, no. 4, pp. 941–973.MathSciNetzbMATHGoogle Scholar
  30. 30.
    Shawe-Taylor, J. and Cristianini, N., Robust Bounds on Generalization from the Margin Distribution, 1998.Google Scholar
  31. 31.
    Reyzin, L. and Schapire, R.E., How boosting the margin can also boost classifier complexity, ICML '06 Proceedings of the 23rd International Conference on Machine Learning, 2006, pp. 753–760.Google Scholar
  32. 32.
    Shen, C. and Li, H., Boosting through optimization of margin distributions, IEEE Trans. Neural Networks, 2010, vol. 21, no. 4, pp. 659–666.CrossRefGoogle Scholar
  33. 33.
    Shen, C. and Li, H., On the dual formulation of boosting algorithms, arXiv:0901.3590 [cs.LG], 2009.Google Scholar
  34. 34.
    Sun, Y., Wang, X., and Tang, X., Deep learning face representation from predicting 10,000 classes, IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 1891–1898.Google Scholar
  35. 35.
    Liu, F., Tang, J., Song, Y., et al., Local structure based multi-phase collaborative representation for face recognition with single sample per person, Inf. Sci., 2016, vols. 346–347, pp. 198–215.Google Scholar
  36. 36.
    Gross, R., Matthews, I., Cohn, J., et al., Multi-PIE, Image Vision Comput., 2010, vol. 28, no. 5, pp. 807–813.CrossRefGoogle Scholar
  37. 37.
    Georghiades, A.S., Belhumeur, P.N., and Kriegman, D.J., From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 2002, no. 6, vol. 23, no. 6, pp. 643–660.CrossRefGoogle Scholar
  38. 38.
    Martinez, A.M., The AR Face Database, CVC Technical Report, 1998.Google Scholar
  39. 39.
    Huang, G.B., Mattar, M., Berg, T., et al., Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments, Amherst: University of Massachusetts, 2007.Google Scholar
  40. 40.
    Lin, G., Xie, M., and Mao, L., Extended CRC: Face recognition with a single training image per person via intraclass variant dictionary, IEICE Trans. Inf. Syst., 2013, vol. 96, no. 10, pp. 2290–2293.CrossRefGoogle Scholar
  41. 41.
    Yang, M., Zhang, L., Yang, J., et al., Robust sparse coding for face recognition, IEEE Computer Vision and Pattern Recognition, 2011, pp. 625–632.CrossRefGoogle Scholar

Copyright information

© Allerton Press, Inc. 2019

Authors and Affiliations

  • Huixian Yang
    • 1
    Email author
  • Weifa Gan
    • 1
  • Fan Chen
    • 1
  • Jinfang Zeng
    • 1
  1. 1.College of Physics and Optoelectronic Engineering Xiangtan UniversityHunanChina

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