Automatically Detected Feature Positions for LBP Based Face Recognition

  • Ladislav Lenc
  • Pavel Král
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 436)

Abstract

This paper presents a novel approach for automatic face recognition based on the Local Binary Patterns (LBP). One drawback of the current LBP based methods is that the feature positions are fixed and thus do not reflect the properties of the particular images. We propose to solve this issue by a method that automatically detects feature positions in the image. These key-points are determined using the Gabor wavelet transform and k-means clustering algorithm. The proposed method is evaluated on two corpora: AT&T Database of Faces and our Czech News Agency (ČTK) dataset containing uncontrolled face images. The recognition rate on the first dataset is 99.5% which represents 2.5% improvement compared to the original LBP method. The best recognition rate obtained on the ČTK corpus is 59.1% whereas the original LBP method reaches only 38.1%.

Keywords

Automatic Face Recognition Czech News Agency Gabor Filter Local Binary Patterns 
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bronstein, A.M., Bronstein, M.M., Spira, A., Kimmel, R.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3022, pp. 225–237. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Bartlett, M.S., Movellan, J.R., Sejnowski, T.J.: Face recognition by independent component analysis. IEEE Transactions on Neural Networks 13(6), 1450–1464 (2002)CrossRefGoogle Scholar
  3. 3.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 711–720 (1997)CrossRefGoogle Scholar
  4. 4.
    Dreuw, P., Steingrube, P., Hanselmann, H., Ney, H., Aachen, G.: Surf-face: Face recognition under viewpoint consistency constraints. In: BMVC, pp. 1–11 (2009)Google Scholar
  5. 5.
    Jain, A.K., Li, S.Z.: Handbook of face recognition. Springer (2005)Google Scholar
  6. 6.
    Kepenekci, B.: Face recognition using gabor wavelet transform. Ph.D. thesis, Citeseer (2001)Google Scholar
  7. 7.
    Lenc, L., Král, P.: Face recognition under real-world conditions. In: International Conference on Agents and Artifitial Intelligence, Barcelona, Spain (2013)Google Scholar
  8. 8.
    Li, W., Fu, P., Zhou, L.: Face recognition method based on dynamic threshold local binary pattern. In: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, pp. 20–24. ACM (2012)Google Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 2 (2004)Google Scholar
  10. 10.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29(1), 51–59 (1996)CrossRefGoogle Scholar
  11. 11.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16(5), 295–306 (1998)CrossRefGoogle Scholar
  12. 12.
    Tan, X., Triggs, B.: Fusing gabor and LBP feature sets for kernel-based face recognition. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 235–249. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19(6), 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Turk, M.A., Pentland, A.P.: Face recognition using eigenfaces. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 1991, pp. 586–591. IEEE (1991)Google Scholar
  15. 15.
    Wiskott, L., Fellous, J.M., Kuiger, N., Von Der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(7), 775–779 (1997)CrossRefGoogle Scholar
  16. 16.
    Xie, Z.: Single sample face recognition based on dct and local gabor binary pattern histogram. In: Huang, D.-S., Bevilacqua, V., Figueroa, J.C., Premaratne, P. (eds.) ICIC 2013. LNCS, vol. 7995, pp. 435–442. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  17. 17.
    Yang, H., Wang, Y.: A lbp-based face recognition method with hamming distance constraint. In: Fourth International Conference on Image and Graphics, ICIG 2007, pp. 645–649. IEEE (2007)Google Scholar
  18. 18.
    Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Transactions on Image Processing 19(2), 533–544 (2010)MathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2014

Authors and Affiliations

  • Ladislav Lenc
    • 1
    • 2
  • Pavel Král
    • 1
    • 2
  1. 1.Dept. of Computer Science and EngineeringUniversity of West BohemiaPlzeňCzech Republic
  2. 2.NTISUniversity of West BohemiaPlzeňCzech Republic

Personalised recommendations