Skip to main content
SpringerLink
Log in

A novel partition selection method for modular face recognition approaches on occlusion problem

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

Recognizing the face with partial occlusion is an important problem for many face recognition applications. Since the occluded parts have no contribution to recognize the face, these parts should be excluded when performing the classification. In this paper, we propose a new method to detect and to use the non-occluded parts of face image for modular face recognition approaches. The occlusion of a partition is decided using the combination of three coefficients which can be easily derived: (i) image entropy, (ii) image correlation, (iii) root-mean-square error. The performance of the proposed partition selection method is tested using the modular extensions of three subspace-based approaches, namely linear regression classification (LRC), common vector approach (CVA), and discriminative common vector approach (DCVA). Modular DCVA is also proposed for the first time in this paper. After the selection of the non-occluded partitions of the face image, LRC, CVA, and DCVA are applied to each of the partitions independently. Then the classifier supports acquired from each of the partitions are combined using three well-known (product, sum, and Borda count) methods to get the final decision. The experiments implemented on the AR and the Extended Yale B face databases show that selection of the face partitions using the proposed strategy improves the recognition accuracy and outperforms state-of-the-art methods.

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

Similar content being viewed by others

References

  1. Cevikalp, H., Neamtu, M., Wilkes, M., Barkana, A.: Discriminative common vectors for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 4–13 (2005)

    Article  Google Scholar 

  2. Comenau, C.A., Simón, M.O., Cohn, J.F., Escalera, S.: Survey on RGB, 3D, thermal, and multimodal approaches for facial expression recognition: history, trends, and affect-related applications. IEEE Trans. Pattern Anal. Mach. Intell. 38(8), 1548–1568 (2006)

    Google Scholar 

  3. Dagnes, N., Vezzetti, E., Marcolin, F., Tornincasa, S.: Occlusion detection and restoration techniques for 3D face recognition: a literature review. Mach. Vis. Appl. 29, 789–813 (2018)

    Article  Google Scholar 

  4. Ding, C., Tao, D.: A comprehensive survey on pose-invariant face recognition. ACM Trans. Intell. Syst. Technol. 7(37), 1–42 (2016)

    Article  Google Scholar 

  5. Georghiades, A.S., Belhumeur, P.N., Kriegman, D.J.: 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 (2001)

    Article  Google Scholar 

  6. Gulmezoglu, M.B., Dzhafarov, V., Keskin, M., Barkana, A.: A novel approach to isolated word recognition. IEEE Trans. Speech Audio Process. 7(6), 620–628 (1999)

    Article  Google Scholar 

  7. Gulmezoglu, M.B., Dzhafarov, V., Barkana, A.: The common vector approach and its relation to principal component analysis. IEEE Trans. Speech Audio Process. 9(6), 655–662 (2001)

    Article  Google Scholar 

  8. Han, H., Shan, S., Chen, X., Gao, W.: A comparative study on illumination preprocessing in face recognition. Pattern Recognit. 43, 1691–1699 (2013)

    Article  Google Scholar 

  9. Jafri, R., Arabnia, H.R.: A survey of face recognition techniques. Int. J. Inf. Process. Syst. 5(2), 41–68 (2009)

    Article  Google Scholar 

  10. Kim, J., Choi, J., Yi, J., Turk, M.: Effective representation using ICA for face recognition robust to local distortion and partial occlusion. IEEE Trans. Pattern Anal. Mach. Intell. 27(12), 1977–1981 (2005)

    Article  Google Scholar 

  11. Kittler, J., Hatef, A., Duin, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)

    Article  Google Scholar 

  12. Koc, M., Barkana, A.: Modular common vector approach. In: Proceedings of the IEEE 22nd Signal Processing and Communications Applications Conference, pp. 533-535. SIU (2014)

  13. Koc, M., Barkana, A.: A novel implementation algorithm for calculation of common vectors. Anadolu Univ. J. Sci. Technol. A Appl. Sci. Eng. 7(2), 251–262 (2016)

    Google Scholar 

  14. Koc, M.: Different decision fusion methods for modular common vector approach. In: Proceedings of the XIVth International Scientific and Technical Conference on Computer Sciences and Information Technologies, pp. 67-70. CSIT (2019)

  15. Koc, M.: Decision fusion for partially occluded face recognition using common vector approach. Chapter In: Advances in Intelligent Systems and Computing IV. 1080, 351–360, Springer (2020)

  16. Lee, K., Ho, J., Kriegman, D.: Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 684–698 (2005)

    Article  Google Scholar 

  17. Li, J., Leng, J., Zhang, W., Guo, M.: Probabilistic robust regression with adaptive weights–a case study on face recognition. Front. Comput. Sci. 14(5), 1–2 (2020)

    Google Scholar 

  18. S. Li, X. Hou, H. Zhang, and Q. Cheng. Learning spatially localized, parts-based representation. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp 1–6, 2001

  19. Martinez, A.M., Benavente, B.: The AR face database. CVC Technical Report No: 24, (1998)

  20. Mi, J.-X., Zhu, Q., Luo, Z.: Matrix regression-based classification with block-norm. Pattern Recognit. Lett. 125, 654–660 (2019)

    Article  Google Scholar 

  21. Nabatchian, A., Abdel-Raheem, E., Ahmadi, M.: Illumination invariant feature extraction and mutual-information-based local matching for face recognition under illumination variation and occlusion. Pattern Recognit. 44(10–11), 2576–2587 (2011)

    Article  Google Scholar 

  22. Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)

    Article  Google Scholar 

  23. Naseem, I., Togneri, R., Bennamoun, M.: Robust regression for face recognition. Pattern Recognit. 45(1), 104–118 (2012)

    Article  Google Scholar 

  24. Nikan, S., Ahmadi, M.: Human face recognition under occlusion using LBP and entropy weighted voting. In: Proceedings of the 21st International Conference on Pattern Recognition, pp 1699–1702. ICPR (2012)

  25. Nikan, S., Ahmadi, M.: Local gradient-based illumination invariant face recognition using local phase quantisation and multi-resolution local binary pattern fusion. IET Image Process 9(1), 12–21 (2015)

    Article  Google Scholar 

  26. Ou, W., Luan, X., Gou, J., Zhou, Q., Xiao, W., Xiong, X., Zeng, W.U.: Robust discriminative nonnegative dictionary learning for occluded face recognition. Pattern Recognit. Lett. 107, 41–49 (2018)

    Article  Google Scholar 

  27. Pentland, A., Moghaddam, B., Starner, T.: View-based and modular eigenspaces for face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 84–91. CVPR (1994)

  28. Peng, Y., Li, L., Liu, S., Li, J., Wang, X.: Extended sparse representation-based classification method for face recognition. Mach. Vis. Appl. 29, 991–1007 (2018)

    Article  Google Scholar 

  29. Pereira, J.F., Barreto, R.M., Cavalcanti, G.D.C., Tsang, I.R.: A robust feature extraction algorithm based on class-Modular Image Principal Component Analysis for face verification. In: Proceedings of the IEEE International Confference Acoustic Speech Signal Processing, pp. 1469–1472. ICASSP (2011)

  30. Polikar, R.: Ensemble based systems in decision making. IEEE Circuits Syst. Mag. 6(3), 21–45 (2006)

    Article  Google Scholar 

  31. Sepas-Moghaddam, A., Pereira, F., Correia, P.L.: Face recognition: a novel multi-level taxonomy based survey (2019). arXiv:1901.00713

  32. Tan, K., Chen, S.: Adaptively weighted sub-pattern PCA for face recognition. Neurocomputing 64, 505–511 (2005)

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Wang, J., Zhang, B., Wang, S., Qi, M., Kong, J.: An adaptively weighted sub-pattern locality preserving projection for face recognition. J. Network Comput. Appl. 33, 323–332 (2010)

    Article  Google Scholar 

  35. Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)

    Article  Google Scholar 

  36. Wu, C.Y., Ding, J.J.: Occluded face recognition using low-rank regression with generalized gradient direction. Pattern Recognit. 80, 256–268 (2018)

    Article  Google Scholar 

  37. Yan, Y., Lee, F., Chen, Q.: Face recognition algorithm using extended vector quantization histogram features. PLoS ONE 13(1), 1–24 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Bilecik Seyh Edebali University, Bilecik, Turkey

    Mehmet Koc

Authors
  1. Mehmet Koc
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mehmet Koc.

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

Koc, M. A novel partition selection method for modular face recognition approaches on occlusion problem. Machine Vision and Applications 32, 35 (2021). https://doi.org/10.1007/s00138-020-01156-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00138-020-01156-4

Keywords

Search

Navigation