Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Advertisement

Springer Nature Link
Log in

Feature selection for spatially enhanced LBP: application to face recognition

  • Regular Paper
  • Published:
International Journal of Data Science and Analytics Aims and scope Submit manuscript

Abstract

Block-based local binary patterns a.k.a. enhanced local binary patterns (ELBPs) have proven to be a highly discriminative descriptor for face recognition and image retrieval. Since this descriptor is mainly composed by histograms, little work (if any) has been done for selecting its relevant features (either the bins or the blocks). In this paper, we address feature selection for both the classic ELBP representation and the recently proposed color quaternionic LBP (QLBP). We introduce a filter method for the automatic weighting of attributes or blocks using an improved version of the margin-based iterative search Simba algorithm. This new improved version introduces two main modifications: (i) the hypothesis margin of a given instance is computed by taking into account the K-nearest neighboring examples within the same class as well as the K-nearest neighboring examples with a different label; (ii) the distances between samples and their nearest neighbors are computed using the weighted \(\chi ^2\) distance instead of the Euclidean one. This algorithm has been compared favorably with several competing feature selection algorithms including the Euclidean-based Simba as well as variance and Fisher score algorithms giving higher performances. The proposed method is useful for other descriptors that are formed by histograms. Experimental results show that the QLBP descriptor allows an improvement of the accuracy in discriminating faces compared with the ELBP. They also show that the obtained selection (attributes or blocks) can either improve recognition performance or maintain it with a significant reduction in the descriptor size.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: recognition using class specific linear projection. In: Buxton, B., Cipolla, R. (eds.) Computer Vision ECCV ’96, Volume 1064 of Lecture Notes in Computer Science, pp. 43–58. Springer, Berlin (1996)

    Google Scholar 

  2. Bishop, C .M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  3. Gilad-Bachrach, R., Amir, N., Naftali, T.: Margin based feature selection—theory and algorithms. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML ’04, p. 43. ACM, New York, NY (2004)

  4. Guan, D., Yuan, W., Lee, Y.-K., Najeebullah, K., Rasel, M.K.: A review of ensemble learning based feature selection. IETE Tech. Rev. 31(3), 190–198 (2014)

    Article  Google Scholar 

  5. Huang, S.H.: Supervised feature selection: a tutorial. Artif. Intell. Res. 4(2), 22–37 (2015)

    Article  Google Scholar 

  6. Hu, L.-Y., Huang, M.-W., Ke, S.-W., Tsai, C.-F.: The distance function effect on k-nearest neighbor classification for medical datasets. SpringerPlus 5(1), 1304 (2016). https://doi.org/10.1186/s40064-016-2941-7

    Article  Google Scholar 

  7. Lan, R., Zhou, Y., Tang, Y.Y., Chen, C.L.P.: Person reidentification using quaternionic local binary pattern. In: 2014 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2014)

  8. Luengo, J., García, S., Herrera, F.: On the choice of the best imputation methods for missing values considering three groups of classification methods. Knowl. Inf. Syst. 32(1), 77–108 (2012). https://doi.org/10.1007/s10115-011-0424-2

    Article  Google Scholar 

  9. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  10. Peng, Z., Gurram, P., Kwon, H., Yin, W.: Sparse kernel learning-based feature selection for anomaly detection. IEEE Trans. Aerosp. Electron. Syst. 51(3), 1698–1716 (2015)

    Article  Google Scholar 

  11. Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: Proceedings of the Second IEEE Workshop on Applications of Computer Vision, 1994, pp. 138–142 (1994)

  12. Sun, Y., Todorovic, S., Goodison, S.: Local learning based feature selection for high dimensional data analysis. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1–18 (2010)

    Article  Google Scholar 

  13. Sun, Y., Wu, D.: Feature extraction through local learning. Stat. Anal. Data Min. 2(1), 34–47 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  14. Sez, J.A., Derrac, J., Luengo, J., Herrera, F.: Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers. Pattern Recognit. 47, 3941–3948 (2014)

    Article  Google Scholar 

  15. Yang, B., Chen, S.: A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image. Neurocomputing 120, 365–379 (2013)

    Article  Google Scholar 

  16. Yang, M., Wang, F., Yang, P.: A novel feature selection algorithm based on hypothesis-margin. J. Comput. 3(12), 27–34 (2008)

    Article  Google Scholar 

  17. Zhou, X., Wang, J.J.: Feature selection for image classification based on a new ranking criterion. J. Comput. Commun. 3, 74–79 (2015)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Signal Theory and Communications Department (DTSC), Carlos III University of Madrid (UC3M), Getafe, Spain

    Abdelmalik Moujahid

  2. University of the Basque Country (UPV/EHU), Leioa, Spain

    Fadi Dornaika

  3. IKERBASQUE, Basque Foundation for Science, Bilbao, Spain

    Fadi Dornaika

Authors
  1. Abdelmalik Moujahid
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fadi Dornaika
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abdelmalik Moujahid.

Ethics declarations

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moujahid, A., Dornaika, F. Feature selection for spatially enhanced LBP: application to face recognition. Int J Data Sci Anal 5, 11–18 (2018). https://doi.org/10.1007/s41060-017-0083-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41060-017-0083-9

Keywords

Search

Navigation