Skip to main content
SpringerLink
Log in

Improved KNN for face classification via high-frequency texture components extraction

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Face classification is an important direction in the face recognition research. Although face classification has been applied in various scenarios, there are still some problems that need to be overcome. Due to the partial occlusion or feeble lighting, the partial information of face will be corrupted, which adversely affects the classification results. According to human cognitive habits, the high-frequency texture component contains the essential features of human faces and can be applied effectively in face classification. Therefore, a method is proposed in this paper to improve the conventional KNN employing high-frequency texture components. The experiment results show that the proposed method outperforms other machine learning methods. Furthermore, the proposed method can even provide similar accuracy to deep learning methods which require much more computational resource.

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
Algorithm 1
Fig. 2
Fig. 3

Similar content being viewed by others

Data Availability

Data openly available in a public repository.

References

  1. Adjabi I, Ouahabi A, Benzaoui A, Taleb-Ahmed A (2020) Past, present, and future of face recognition: a review. Electronics 9(8):1188

    Article  Google Scholar 

  2. Ameur B, Masmoudi S, Derbel AG, Ben Hamida A (2016) Fusing gabor and lbp feature sets for knn and src-based face recognition. In: 2016 2nd International conference on advanced technologies for signal and image processing (ATSIP), pp 453–458. https://doi.org/10.1109/ATSIP.2016.7523134

  3. Anyanwu MN, Sajjan S (2009) Comparative analysis of serial decision tree classification algorithms. Int J Comput Sci Secur 3(3):230–240

    Google Scholar 

  4. Biswas S, Sil J (2020) An efficient face recognition method using contourlet and curvelet transform. J King Saud Univ-Comput Inf Sci 32(6):718–729

    Google Scholar 

  5. Chi H, Xia H, Zhang L, Zhang C, Tang X (2020) Competitive and collaborative representation for classification. Pattern Recogn Lett 132:46–55

    Article  Google Scholar 

  6. Damer N, Grebe JH, Chen C, Boutros F, Kirchbuchner F, Kuijper A (2020) The effect of wearing a mask on face recognition performance: an exploratory study. In: 2020 International conference of the biometrics special interest group (BIOSIG), pp 1–6

  7. Deng K, Peng Z, Zhu W (2020) A discriminative projection and representation-based classification framework for face recognition. SIAM J Imaging Sci 13(3):1446–1466

    Article  MathSciNet  Google Scholar 

  8. Georghiades AS, Belhumeur PN, Kriegman DJ (2001) From few to many: illumination cone models for face recognition under variable lighting and pose. IEEE Trans Pattern Anal Mach Intell 23(6):643–660

    Article  Google Scholar 

  9. Green J, Staff L, Bromley P, Jones L, Petty J (2021) The implications of face masks for babies and families during the covid-19 pandemic: a discussion paper. J Neonatal Nursing 27(1):21–25

    Article  Google Scholar 

  10. Guo G, Li SZ, Chan K (2000) Face recognition by support vector machines. In: Proceedings of Fourth IEEE International Conference on Automatic Face and Gesture Recognition, pp 196–201

  11. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  12. Hong C, Yu J, Zhang J, Jin X, Lee K-H (2018) Multimodal face-pose estimation with multitask manifold deep learning. IEEE Trans Industr Inf 15(7):3952–3961

    Article  Google Scholar 

  13. Jia S, Deng X, Zhu J, Xu M, Zhou J, Jia X (2019) Collaborative representation-based multiscale superpixel fusion for hyperspectral image classification. IEEE Trans Geosci Remote Sensing 57(10):7770–7784

    Article  Google Scholar 

  14. Kortli Y, Jridi M, Al Falou A, Atri M (2020) Face recognition systems: a survey. Sensors 20(2):342

    Article  Google Scholar 

  15. Kramer O (2013) K-nearest neighbors. Springer Berlin Heidelberg

  16. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst, vol 25

  17. Martinez A, Benavente R (1998) The ar face database. Tech Rep 24 CVC Technical Report

  18. Negi A, Chauhan P, Kumar K, Rajput R (2020) Face mask detection classifier and model pruning with keras-surgeon. In: 2020 5th IEEE international conference on recent advances and innovations in engineering (ICRAIE). IEEE, pp 1–6

  19. Negi A, Kumar K (2021) Face mask detection in real-time video stream using deep learning. Computat Intell Healthcare Inf:255–268

  20. Nejrs S, Al-Ani M (2020) Face image classification based on feature extraction. Solid State Technol 63:13515–13526

    Google Scholar 

  21. Noyes E, Davis JP, Petrov N, Gray KL, Ritchie KL (2021) The effect of face masks and sunglasses on identity and expression recognition with super-recognizers and typical observers. R Soc Open Sci 8(3):201169

    Article  Google Scholar 

  22. Peterson LE (2009) K-nearest neighbor. Scholarpedia 4(2):1883

    Article  Google Scholar 

  23. Rokach L, Maimon O (2005) Decision trees. IEEE Trans Syst Man Cybern Part 35(4):476–487

    Article  MATH  Google Scholar 

  24. Sasankar P, Kosarkar U (2021) A study for face recognition using techniques pca and knn. EasyChair

  25. Sasirekha K, Thangavel K (2019) Optimization of k-nearest neighbor using particle swarm optimization for face recognition. Neural Comput Appl 31 (11):7935–7944

    Article  Google Scholar 

  26. Sharma S, Kumar K, Singh N (2017) D-fes: deep facial expression recognition system. In: 2017 Conference on information and communication technology (CICT), pp 1–6. https://doi.org/10.1109/INFOCOMTECH.2017.8340635https://doi.org/10.1109/INFOCOMTECH.2017.8340635

  27. Sim T, Baker S, Bsat M (2001) The cmu pose, illumination, and expression (pie) database of human faces. Technical Report CMU-RI-TR-01-02, Carnegie Mellon University, Pittsburgh PA

  28. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  29. Solanki K, Pittalia P (2016) Review of face recognition techniques. Int J Comput Appl 133(12):20–24

    Google Scholar 

  30. Sun Y, Wang X, Tang X (2013) Hybrid deep learning for face verification. In: Proceedings of the IEEE international conference on computer vision, pp 1489–1496

  31. Wang S, Ge H, Yang J, Tong Y, Su S (2021) Reciprocal kernel-based weighted collaborative–competitive representation for robust face recognition. Mach Vis Appl, vol 32(1)

  32. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell

  33. Xs A, Yc A, Zhf B, Gh B, Tao ZA, Xjw A (2019) Collaborative representation based face classification exploiting block weighted lbp and analysis dictionary learning. Pattern Recogn 88:127–138

    Article  Google Scholar 

  34. Yin H-F, Wu X-J (2020) Class-specific residual constraint non-negative representation for pattern classification. J Electr Imaging 29(2):023014

    Article  Google Scholar 

  35. Zhang H, Chen G (2012) The research of face recognition based on pca and k-nearest neighbor. In: 2012 Symposium on photonics and optoelectronics. IEEE, pp 1–4

  36. Zhang D, Zhou Z-H (2005) (2D) 2pca: two-directional two-dimensional pca for efficient face representation and recognition. Neurocomputing 69:224–231. https://doi.org/10.1016/j.neucom.2005.06.004

    Article  Google Scholar 

  37. Zhang R, Zheng Y (2018) A survey on biometric authentication: toward secure and privacy-preserving identification. IEEE Access

  38. Zheng S, Ding C (2020) A group lasso based sparse knn classifier. Pattern Recogn Lett 131:227–233

    Article  Google Scholar 

  39. Zhu Y, Xue J (2017) Face recognition based on random subspace method and tensor subspace analysis. Neural Comput Appl 28(2):233–244

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grants 62001127, Industry-University Research Collaboration Project Funded by Zhuhai City (No. ZH22017001200095PWC) and Guangzhou Basic and Applied Basic Research Project under grant 202102020701. The authors declare that there is no conflict of interests regarding the publication of this article.

Author information

Authors and Affiliations

  1. School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510000, China

    Dakang Liu, Zexiao Liang, Yuan Liu & Jianzhong Li

  2. School of Automation, Guangdong University of Technology, Guangzhou, 510000, China

    Dakang Liu

  3. System Operations Control Center, China Southern Airlines Co.Ltd, Guangzhou, 510470, China

    Wenlang Li

Authors
  1. Dakang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zexiao Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenlang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yuan Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jianzhong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jianzhong Li.

Ethics declarations

Conflict of Interests

All authors have no conflicts of interest.

Additional information

Publisher’s note

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

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, D., Liang, Z., Li, W. et al. Improved KNN for face classification via high-frequency texture components extraction. Multimed Tools Appl 82, 18585–18597 (2023). https://doi.org/10.1007/s11042-022-14244-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-022-14244-6

Keywords

Search

Navigation