Skip to main content
SpringerLink
Log in

Dictionary learning and face recognition based on sample expansion

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

Dictionary learning has become a research hotspot. How to construct a robust dictionary is a key issue. In face recognition problem, differences in expressions, postures, and lighting conditions are key factors that affect the accuracy. Therefore, images of the same face can be very different in different situations. In real-world scenario, the samples of each face are very limited, which make it hard for the network to generalize well. Therefore, To solve the problem mentioned above, this paper proposes a method to construct a robust dictionary. In the method, virtual samples are generated to appropriately reflect the diversity of the face images, and based on this, two dictionaries are constructed and a corresponding fusion classification scheme is designed. The main advantages of this method are as follows: firstly, the simultaneous use of virtual samples and original samples can better reflect the facial appearance of each morphology, and the dictionaries obtained help to improve the robustness of the dictionary learning algorithm. Secondly, the proposed fusion classification scheme can give full play to the performance of the double dictionary learning algorithm. The results of out experiments show that the proposed algorithm is superior to some existing 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
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Liu Y, Lasang P, Siegel M et al (2016) Multi-sparse descriptor: a scale invariant feature for pedestrian detection[J]. Neurocomputing 184:55–65

    Article  Google Scholar 

  2. Vu TH, Monga V (2017) Fast low-rank shared dictionary learning for image classification[J]. IEEE Transactions on Image Processing PP(99):1–1

    MathSciNet  MATH  Google Scholar 

  3. Abrol V, Sharma P, Sao AK (2016) Greedy dictionary learning for kernel sparse representation based classifier[J]. Pattern Recogn Lett 78(C):64–69

    Article  Google Scholar 

  4. Bi C, Yi Y, Zhang L et al (2020) Jointly learning the discriminative dictionary and projection for face recognition[J]. Mathematical Problems in Engineering, 2020. https://doi.org/10.1155/2020/1527965

  5. Xu Y, Li Z, Zhang B et al (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition[J]. Inf Sci 375(C):171–182

    Article  Google Scholar 

  6. Lin G, Meng Y, Jian Y et al (2018) Robust, discriminative and comprehensive dictionary learning for face recognition[J]. Pattern Recogn 81:S0031320318301109

    Article  Google Scholar 

  7. Varasteh Yazdi S, Douzal-Chouakria A (2018) Time Warp Invariant k-SVD: sparse coding and dictionary learning for time series under time warp[J]. Pattern Recogn Lett 112:1–8

    Article  Google Scholar 

  8. Gz A, Fp B, Hs C et al (2020) Cost-sensitive joint feature and dictionary learning for face recognition-ScienceDirect[J]. Neurocomputing 391:177–188

    Article  Google Scholar 

  9. Xu Y, Li Z, Yang J et al (2017) A survey of dictionary learning algorithms for face recognition[J]. IEEE Access 5:8502– 8514

    Article  Google Scholar 

  10. Shahram T, Önsen T (2018) Animal classification using facial images with score-level fusion[J]. IET Comput Vis 12(5):679– 685

    Article  Google Scholar 

  11. Duan X, Tan ZH (2018) A spatial self-similarity based feature learning method for face recognition under varying poses[J]. Pattern Recogn Lett 111:109–116

    Article  Google Scholar 

  12. Luo X, Xu Y, Yang J, Chen Z, Wu X, Kittler J (2019) Multi-resolution dictionary learning for face recognition[J]. Pattern Recognition 93:283–292

    Article  Google Scholar 

  13. Chen Z, Wu XJ, Yin HF et al (2019) Noise-robust dictionary learning with slack block-diagonal structure for face recognition[J]. Pattern Recognition 100:107118

    Article  Google Scholar 

  14. Lin J, Wu X, Yan X (2016) Improved PCA-BP face recognition based on construction of virtual Sample.[C]// International conference on intelligent data engineering and automated learning. Springer International Publishing

  15. Liu C (2019) A survey of virtual sample generation technology for face recognition[J]. Comput. Rev. 60(5):220–221

    Google Scholar 

  16. Yang J, Liu Y (2017) Undersampled face recognition based on virtual samples and representation classification[J]. Neural Computing and Applications 31(7):2447–2453

    Article  Google Scholar 

  17. Yong X, Zhu X, Li Z et al (2013) Using the original and sym- metrical face training samples to perform representation based two-step face recognition[J]. Pattern Recognition 46:4

    Google Scholar 

  18. Li Z, Lai Z, Yong X et al (2017) A locality-constrained and label embedding dictionary learning algorithm for image classification[J] Neural Networks and Learning Systems. IEEE Transactions on 28(2):278–293

    Article  MathSciNet  Google Scholar 

  19. Ghazvininejad M (2013) From local similarity to global coding: an application to image classification[C]// Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society

  20. Martinez A, Benavente R (1998) The AR face database. Technical report 24, Computer Vision Center, Barcelona, Spain

  21. Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. Technical report 07-49, College Computer Science, University of Massachusetts, Amherst, MA, USA

  22. Samaria FS, Harter AC (1994) Parameterisation of a stochastic model for human face identification. In: Proceedings of 1994 IEEE workshop on applications of computer vision. IEEE Comput. Soc. Press, pp 138–142

  23. Xu Y, Li XL, Yang J, Lai ZH, Zhang D (2014) Integrating conventional and inverse representation for face recognition. IEEE Trans Cybern 44(10):1738–1746

    Article  Google Scholar 

  24. Fang XZ, Xu Y, Li XL, Lai ZH, Wong WK (2015) Learning a nonnegative sparse graph for linear regression. IEEE Trans Image Process 24(9):2760–2771

    Article  MathSciNet  Google Scholar 

  25. Aharon M, Elad M, Bruckstein A (2006) K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing 54(11):4311–4322

    Article  Google Scholar 

  26. Zhang Q, Li B (2010) Discriminative k-SVD for dictionary learning in face recognition[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE

  27. Jiang Z, Lin Z, Davis LS (2013) Label consistent k-SVD: learning a discriminative dictionary for recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(11):2651–2664

    Article  Google Scholar 

  28. Chen Z, Wu X, Kittler J (2019) Non-negative representation based discriminative dictionary learning for face recognition[C]. In: 10th international conference on image and graphics, ICIG. 2019(11901):307–319

  29. Li L, Peng Y, Liu S (2020) Compound dictionary learning based classification method with a novel virtual sample generation Technology for Face Recognition[J]. Multimedia Tools and Applications 79 (31-32):23325–23346

    Article  Google Scholar 

  30. Zhao Z, Feng G, Zhang L et al (2019) Novel orthogonal based collaborative dictionary learning for efficient face recognition[J]. Knowl-Based Syst 163:533–545

    Article  Google Scholar 

  31. Sla B, Yw B, Xw A et al (2020) Discriminative dictionary learning algorithm based on sample diversity and locality of atoms for face recognition-ScienceDirect[J]. J Vis Commun Image Represent, 71

  32. Zheng S, Zhang Y, Liu W et al (2020) A dictionary learning algorithm based on dictionary reconstruction and its application in face recognition[J]. Math Probl Eng, 2020. https://doi.org/10.1155/2020/8964321

Download references

Acknowledgements

This work was supported by the Research Foundation for Advanced Talents of Guizhou University under grant: (2016) No. 49, Key Disciplines of Guizhou Province - Computer Science and Technology (ZDXK [2018]007), Key Supported Disciplines of Guizhou Province - Computer Application Technology (No. QianXueWeiHeZi ZDXK[2016]20), and the work was also supported by National Natural Science Foundation of China (61462013, 61661010) and 2017 Zhuhai introduces innovation and entrepreneurship team (ZH01110405170027PWC).

Author information

Authors and Affiliations

  1. Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province, College of Computer Science and Technology, Guizhou University, Guiyang, China

    Yongjun Zhang, Wenjie Liu, Yongjie Zou & Qian Wang

  2. Zhuhai Orbita Aerospace Science And Technology Co., LTD. Oribita Tech Park, 1 BaiSha Road, TangJia DongAn, ZhuHai, China

    Haisheng Fan

  3. Big Data Science and Intelligent Engineering Research Institute, Guizhou Education University, Guiyang, 550018, China

    Zhongwei Cui

Authors
  1. Yongjun Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Wenjie Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Haisheng Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongjie Zou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhongwei Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Qian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Yongjun Zhang or Haisheng Fan.

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, Y., Liu, W., Fan, H. et al. Dictionary learning and face recognition based on sample expansion. Appl Intell 52, 3766–3780 (2022). https://doi.org/10.1007/s10489-021-02557-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-021-02557-2

Keywords

Search

Navigation