Skip to main content
SpringerLink
Log in

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

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.

Similar content being viewed by others

REFERENCES

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  4. Ye, H., Li, Y., Chen, C., et al., Fast fisher discriminant analysis with randomized algorithms, Pattern Recognit., 2017, vol. 72, pp. 82–92.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

  8. Simonyan, K. and Zisserman, A., Very deep convolutional networks for large-scale image recognition, arXiv:1409.1556 [cs.CV], 2014.

  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.

  10. Srivastava, R.K., Greff, K., and Schmidhuber, J., Training very deep networks, arXiv:1507.06228 [cs.LG], 2015.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

    Article  Google Scholar 

  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.

  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.

    Article  Google Scholar 

  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.

  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.

  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.

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  MathSciNet  Google Scholar 

  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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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.

  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.

    MathSciNet  MATH  Google Scholar 

  30. Shawe-Taylor, J. and Cristianini, N., Robust Bounds on Generalization from the Margin Distribution, 1998.

  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.

  32. Shen, C. and Li, H., Boosting through optimization of margin distributions, IEEE Trans. Neural Networks, 2010, vol. 21, no. 4, pp. 659–666.

    Article  Google Scholar 

  33. Shen, C. and Li, H., On the dual formulation of boosting algorithms, arXiv:0901.3590 [cs.LG], 2009.

  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.

  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. Gross, R., Matthews, I., Cohn, J., et al., Multi-PIE, Image Vision Comput., 2010, vol. 28, no. 5, pp. 807–813.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  38. Martinez, A.M., The AR Face Database, CVC Technical Report, 1998.

  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. 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.

    Article  Google Scholar 

  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.

    Book  Google Scholar 

Download references

Funding

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

Author information

Authors and Affiliations

  1. College of Physics and Optoelectronic Engineering Xiangtan University, Hunan, China

    Huixian Yang, Weifa Gan, Fan Chen & Jinfang Zeng

Authors
  1. Huixian Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Weifa Gan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fan Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jinfang Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Huixian Yang.

Ethics declarations

The authors declare that they have no conflicts of interest.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Huixian Yang, Gan, W., Chen, F. et al. Cropped and Extended Patch Collaborative Representation Face Recognition for a Single Sample Per Person. Aut. Control Comp. Sci. 53, 550–559 (2019). https://doi.org/10.3103/S0146411619060099

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411619060099

Keywords:

Search

Navigation