Skip to main content
SpringerLink
Log in

Dissimilarity-based nearest neighbor classifier for single-sample face recognition

  • Original Article
  • Published:
The Visual Computer Aims and scope Submit manuscript

Abstract

In single-sample face recognition (SSFR) tasks, the nearest neighbor classifier (NNC) is the most popular method for its simplicity in implementation. However, in complex situations with light, posture, expression, and obscuration, NNC cannot achieve good recognition performance when applying common distance measurements, such as the Euclidean distance. Thus, this paper proposes a novel distance measurement scheme for NNC and applies it to SSFR. The proposed method, called dissimilarity-based nearest neighbor classifier (DNNC), first segments each (training or test) image into non-overlapping patches with a given size and then generates an ordered image patch set. The dissimilarities between the given test image patch set and the training image patch sets are computed and taken as the distance measurement of NNC. The smaller the dissimilarity of image patch sets is, the closer is the distance from the test image to the training image. Therefore, the category of the test image can be determined according to the smallest dissimilarity. Extensive experiments on the AR face database demonstrate the effectiveness of DNNC, especially for the case of obscuration.

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. Cai, Y., Xu, C.Y., Fan, J.L.: Application research of PCA algorithm for face recognition. Inf. Technol. 11, 129–132 (2017)

    Google Scholar 

  2. Zhou, S.F., Dai, S.G.: Research on improved PCA face recognition algorithm. Softw. Guide 2, 15–18 (2018)

    Google Scholar 

  3. Zhou, B., He, Y.Q., Wang, J.: Face recognition based on adaptive near-part local preserving projection algorithm. In: Advances in Laser and Optoelectronics, vol. 3 (2018)

  4. Lin, J., Wu, X.A.: Research and application of improved face recognition algorithm based on virtual sample. Comput. Eng. Appl. 23, 123–128 (2017)

    Google Scholar 

  5. Wu, H.P., Dai, S.K.: Face recognition of 2 DLDA based on ULBP eigensubspace. Pattern Recog. Artif. Intell. 10, 894–899 (2014)

    Google Scholar 

  6. Qin J., He Z.S.: A SVM face recognition method based on Gabor-featured key points. In: International Conference on Machine Learning and Cybernetics, vol. 8 (2005)

  7. Cheng, Y., Jiao, L.B., Cao, X.H., Li, Z.Y.: Illumination-insensitive features for face recognition. Vis. Comput. 33, 1483–1493 (2017)

    Google Scholar 

  8. Liu, F., Bi, Y., Cui, Y., Tang, Z.M.: Local similarity based linear discriminant analysis for face recognition with single sample per person. In: Asian Conference on Computer Vision, vol. 9010, pp. 85–95 (2015)

  9. Li, J.X., Zhang, S.M., Wu, H.S.: Single training sample face recognition based on image blocking and feature selection. Comput. Appl. Softw. 9, 310–313 (2015)

    Google Scholar 

  10. Pan, J., Wang, X.S., Cheng, Y.H.: Single-sample face recognition based on LPP feature transfer. IEEE Access 4, 2873–2884 (2016)

    Google Scholar 

  11. Wei, M.J., Xu, D.Y., Qin, Y.B.: Face recognition based on automatic weighted K nearest patches for single training sample. J. Front. Comput. Sci. Technol. 9, 1505–1512 (2017)

    Google Scholar 

  12. Pei, T.W., Zang, L., Wang, B.J., et al.: Decision pyramid classifier for face recognition under complex variations using single sample per person. Pattern Recogn. 64, 305–313 (2017)

    Google Scholar 

  13. Ding, C., Bao, T., 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)

    Google Scholar 

  14. Chu, Y.J., Zhao, L.D., Ahmad, T.: Multiple feature subspaces analysis for single sample per person face recognition. Vis. Comput. 35(2), 239–256 (2019)

    Google Scholar 

  15. Yu, Y., Wang, J.H., Sun, H.J.: On applying adaptive generic learning framework to face recognition. Comput. Appl. Softw. 7, 173–176 (2014)

    Google Scholar 

  16. Zhang, Z., Li, F.Z., Chow, T., et al.: Sparse codes auto-extractor for classification: a joint embedding and dictionary learning framework for representation. IEEE Trans. Signal Process. 64(14), 3790–3805 (2016)

    MathSciNet  MATH  Google Scholar 

  17. Zhang, Z., Jiang, W.M., Qin, J., et al.: Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Trans. Neural Netw. Learn. Syst. 29(8), 3798–3814 (2018)

    MathSciNet  Google Scholar 

  18. Zhang, Z., Ren, J.H., Jiang, W.M., et al.: Joint subspace recovery and enhanced locality driven robust flexible discriminative dictionary learning. IEEE Trans. Circuits Syst. Video Technol. (2019)

  19. Lu, J., Tan, Y.P., Wang, G.: Discriminative multimanifold analysis for face recognition from a single training sample per person. IEEE Trans. Pattern Anal. Mach. Intell. 35(1), 39–51 (2013)

    Google Scholar 

  20. Mehrasa, N., Aghagolzadeh, A., Motameni, H.: A supervised multimanifold method with locality preserving for face recognition using single sample per person. J. Cent. South Univ. 24(12), 2853–2861 (2017)

    Google Scholar 

  21. Zhuang, L., Chan, T.H., Yang, A.Y., et al.: Sparse illumination learning and transfer for single-sample face recognition with image corruption and misalignment. IEEE Comput. Vis. Pattern Recognit. 114(2–3), 3546–3553 (2013)

    Google Scholar 

  22. Ji, H.K., Sun, Q.S., Ji, Z.X., et al.: Collaborative probabilistic labels for face recognition from single sample per person. Pattern Recogn. 62(C), 125–134 (2017)

    Google Scholar 

  23. Yu, Y.F., Dai, D.Q., Ren, C.X., et al.: Discriminative multi-scale sparse coding for single-sample face recognition with occlusion. Pattern Recogn. 66, 320–312 (2017)

    Google Scholar 

  24. Wang, H., Zhang, D.S., Miao, Z.H.: Face recognition with single sample per person using HOG-LDB and SVDL. Signal Image Video Process. 13(5), 985–992 (2019)

    Google Scholar 

  25. Kano, M., Hasebe, S., Hashimoto, I., et al.: Statistical process monitoring based on dissimilarity of process data. AIChE J. 6, 1231–1240 (2002)

    Google Scholar 

  26. Ding, S.F., Xu, X., Fan, S.Y., Xue, Y.: Locally adaptive multiple kernel k-means algorithm based on shared nearest neighbors. Soft. Comput. 22, 4573–4583 (2018)

    Google Scholar 

  27. Gou, J.P., Ma, H.X., Ou, W.H., et al.: A generalized mean distance-based k-nearest neighbor classifier. Expert Syst. Appl. 115, 356–372 (2019)

    Google Scholar 

  28. Martinez A.M., Benavente R.: The AR face database. CVC Technical Report #24 (1998)

  29. Zhang, L., Yang, M., Feng, X.: Sparse representation or collaborative representation: which helps face recognition? In: International Conference on Computer Vision. IEEE, pp. 471–478 (2012)

  30. Yang, M., Van, L., Zhang, L.: Sparse variation dictionary learning for face recognition with a single training sample per person. In: IEEE International Conference on Computer Vision, pp. 689–696 (2013)

  31. Seetafaceengine. https://github.com/seetaface/SeetaFaceEngine (2016)

  32. Zhu, P., Yang, M., Zhang, L., Lee, I.Y.: Local generic representation for face recognition with single sample per person. In: Asian Conference on Computer Vision, pp. 34–50 (2014)

  33. Turk, M., Pentland, A.: Eigenfaces for recognition. Cognit. Neurosci. 3(1), 71–86 (1991)

    Google Scholar 

  34. Wu, J., Zhou, Z.: Face recognition with one training image per person. Pattern Recogn. Lett. 23(14), 1711–1719 (2002)

    MATH  Google Scholar 

  35. Chen, S., Zhang, D., Zhou, Z.: Enhanced \(\rm (PC)^{2}\)A for face recognition with one training image per person. Pattern Recogn. Lett. 25(10), 1173–1181 (2004)

    Google Scholar 

  36. Yang, J., Zhang, D., Frangi, A., et al.: Two-dimensional PCA: a new approach to appearance-based face representation and recognition. IEEE Trans. Pattern Anal. Mach. Intell. 26(1), 131–137 (2004)

    Google Scholar 

  37. Zhang, D., Zhou, Z.: \(\rm (2D)^{2}\)PCA: two-directional two-dimensional PCA for efficient face representation and recognition. Neurocomputing 69(1–3), 224–231 (2005)

    Google Scholar 

  38. Tan, X., Chen, S., Zhou, Z., et al.: Recognizing partially occluded, expression variant faces from single training image per person with SOM and soft K-NN ensemble. IEEE Trans. Neural Netw. 16(4), 875–886 (2005)

    Google Scholar 

  39. He, X., Yan, S., Hu, Y., et al.: Face recognition using Laplacianfaces. IEEE Trans. Pattern Anal. Mach. Intell. 27(3), 328–340 (2005)

    Google Scholar 

  40. Zhang, D., Chen, S., Zhou, Z.: A new face recognition method based on SVD perturbation for single example image per person. Appl. Math. Comput. 163(2), 895–907 (2005)

    MathSciNet  MATH  Google Scholar 

  41. Gottumukkal, R., Asari, V.: An improved face recognition technique based on modular PCA approach. Pattern Recogn. Lett. 25(4), 429–436 (2004)

    Google Scholar 

  42. Chen, S., Liu, J., Zhou, Z.H.: Making FLDA applicable to face recognition with one sample per person. Pattern Recogn. 37(7), 1553–1555 (2004)

    Google Scholar 

  43. Deng, W., Hu, J., Guo, J., et al.: Robust, accurate and efficient face recognition from a single training image: a uniform pursuit approach. Pattern Recogn. 43(5), 1748–1762 (2010)

    MATH  Google Scholar 

  44. Ji, H.K., Sun, Q.S., Ji, Z.X., et al.: Collaborative probabilistic labels for face recognition from single sample per person. Pattern Recogn. 62(C), 125–134 (2017)

    Google Scholar 

  45. Wright, J., Yang, A., V, A., et al.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Google Scholar 

  46. Deng, W., Hu, J., Guo, J.: Extended SRC: undersampled face recognition via intraclass variant dictionary. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1864–70 (2012)

    Google Scholar 

  47. Ding, R.X., Du, D.K., Huang, Z.H., et al.: Variational feature representation-based classification for face recognition with single sample per person. J. Visual Commun. Image Represent. 30, 35–45 (2015)

    Google Scholar 

  48. Zhang, L., Zhou, W.D.: Sparse ensembles using weighted combination methods based on linear programming. Pattern Recogn. 44(1), 97–106 (2011)

    MATH  Google Scholar 

  49. Fakhari, F., Mosavi, M.R., Lajvardi, M.M.: Image fusion based on multi-scale transform and sparse representation: an image energy approach. IET Image Proc. 11(11), 1041–1049 (2017)

    Google Scholar 

  50. Xiang, F.T., Jian, Z., Liang, P., et al.: Robust image fusion with block sparse representation and online dictionary learning. IET Image Proc. 12(3), 345–353 (2018)

    Google Scholar 

Download references

Acknowledgements

We would like to thank three anonymous reviewers and Editor Nadia Magnenat-Thalmann for their valuable comments and suggestions, which have significantly improved this paper. This work was supported in part by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China under Grant No. 19KJA550002, by the Six Talent Peak Project of Jiangsu Province of China under Grant No. XYDXX-054, by the Priority Academic Program Development of Jiangsu Higher Education Institutions, and by the Collaborative Innovation Center of Novel Software Technology and Industrialization.

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Joint International Research Laboratory of Machine Learning and Neuromorphic Computing, Soochow University, Suzhou, 215006, Jiangsu, China

    Zhengqi Zhang, Li Zhang & Meng Zhang

  2. Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, Suzhou, 215006, Jiangsu, China

    Li Zhang

Authors
  1. Zhengqi Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Meng Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Li Zhang.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Zhang, L. & Zhang, M. Dissimilarity-based nearest neighbor classifier for single-sample face recognition. Vis Comput 37, 673–684 (2021). https://doi.org/10.1007/s00371-020-01827-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00371-020-01827-3

Keywords

Search

Navigation