Face recognition with single sample per person using HOG–LDB and SVDL

  • Hua Wang
  • DingSheng ZhangEmail author
  • ZhongHua Miao
Original Paper


The recognition rate of some face recognition methods that require a certain number of samples will be significantly reduced in case only one sample is available for training. Aim at this situation, a new feature extraction method, HOG–LDB (histogram of oriented gradients–local difference binary), is proposed. Then, we combined this method with SVDL (sparse variation dictionary learning) to recognize the probe images with different facial variations (e.g., illuminations, poses, expressions and disguises). The descriptor of HOG–LDB can extract the edge features and local pattern features of the image. After the feature extraction, SVDL is employed in the generic training set and the generic variation dictionary is obtained. Then, the dictionary is used for predicting the subjects of the probe images with different facial variations. Finally, experimental results on the AR dataset, the Yale dataset, the Extended Yale B dataset and the CMU-PIE dataset proved the validity of the proposed method.


HOG–LDB Sparse variation dictionary learning Single sample per person Face recognition 



  1. 1.
    Radman, A., Suandi, S.A.: Robust face pseudo-sketch synthesis and recognition using morphological-arithmetic operations and HOG-PCA. Multimedia Tools Appl. 77(19), 25311–25332 (2018)CrossRefGoogle Scholar
  2. 2.
    Bagherzadeh, S.A.Z., Sarcheshmeh, A.N., Bagherzadeh, S.H.Z., et al.: A new hybrid face recognition algorithm based on discrete wavelet transform and direct LDA. In: Biomedical Engineering and International Iranian Conference on Biomedical Engineering (2017)Google Scholar
  3. 3.
    Wen, Y.: A novel dictionary based SRC for face recognition. In: Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on, pp. 2582–2586 (2017)Google Scholar
  4. 4.
    Gao, Q., Zhang, L., Zhang, D.: Face recognition using FLDA with single training image per person. Appl. Math. Comput. 205(2), 726–734 (2008)zbMATHGoogle Scholar
  5. 5.
    Rahim, A., Azam, S., Hossain, N.: Face recognition using local binary patterns (LBP). Glob. J. Comput. Sci. Technol. 13(4), 1–8 (2013)Google Scholar
  6. 6.
    Yan, H., Lu, J., Zhou, X.: Multi-feature multi-manifold learning for single-sample face recognition. Neurocomputing 143(16), 134–143 (2014)CrossRefGoogle Scholar
  7. 7.
    Oh, B.S., Toh, K.A., Teoh, A.B.J., Lin, Z.: An analytic gabor feedforward network for single-sample and pose-invariant face recognition. IEEE Trans. Image Process. 27(6), 2791–2805 (2018)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Kumar, G., Arivazhagan, A.: Mammogram classification based on k-nearest neighbor classifier. Indian J Public Health Res Dev 8(3), 451–453 (2017)Google Scholar
  9. 9.
    Ding, C.H., Bao, T.L., Karmoshi, S., et al.: Single sample per person face recognition with KPCANet and a weighted voting scheme. Signal Image Video Process 11(7), 1213–1220 (2017)CrossRefGoogle Scholar
  10. 10.
    Deng, W.H., Hu, J.N., Guo, J.: Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1864–1870 (2012)CrossRefGoogle Scholar
  11. 11.
    Parisa Beham, M., Mansoor Roomi, S.M.: Anti-spoofing enabled face recognition based on aggregated local weighted gradient orientation. Signal Image Video Process. 12(3), 531–538 (2018)CrossRefGoogle Scholar
  12. 12.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 886–893 (2005)Google Scholar
  13. 13.
    Farid, H.: Blind inverse gamma correction. IEEE Trans. Image Process. 10(10), 1428–1433 (2001)CrossRefzbMATHGoogle Scholar
  14. 14.
    Schechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2007)Google Scholar
  15. 15.
    Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)CrossRefGoogle Scholar
  16. 16.
    Wan, Y., Li, H.H., Wu, K.F., et al.: Fusion with layered features of LBP and HOG for face recognition. J. Comput. Aided Des. Comput. Graph. 27(4), 640–650 (2015)Google Scholar
  17. 17.
    Gu, J.Q., Hu, H.F., Li, H.X.: Local robust sparse representation for face recognition with single sample per person. IEEE/CAA J. Autom. Sinica 5(2), 547–554 (2018)CrossRefGoogle Scholar
  18. 18.
    Yang, M., Van, L., Zhang, L.: Sparse variation dictionary learning for face recognition with a single training sample per person. In: ICCV ‘13 Proceedings of the 2013 IEEE International Conference on Computer Vision, pp. 689–696 (2013)Google Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Shanghai Key Laboratory of Power Station Automation Technology, School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina

Personalised recommendations