Pixelwise Local Binary Pattern Models of Faces Using Kernel Density Estimation

  • Timo Ahonen
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

Local Binary Pattern (LBP) histograms have attained much attention in face image analysis. They have been successfully used in face detection, recognition, verification, facial expression recognition etc. The models for face description have been based on LBP histograms computed within small image blocks. In this work we propose a novel, spatially more precise model, based on kernel density estimation of local LBP distributions. In the experiments we show that this model produces significantly better performance in the face verification task than the earlier models. Furthermore, we show that the use of weighted information fusion from individual pixels based on a linear support vector machine provides with further improvements in performance.

Keywords

Face Recognition Facial Image Local Binary Pattern Face Detection Kernel Density Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Li, S.Z., Jain, A.K. (eds.): Handbook of Face Recognition. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Analysis and Machine Intelligence 24(7), 971–987 (2002)Google Scholar
  3. 3.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face description with local binary patterns: Application to face recognition. IEEE Trans. Pattern Analysis and Machine Intelligence 28(12), 2037–2041 (2006)Google Scholar
  4. 4.
    Hadid, A., Pietikäinen, M., Ahonen, T.: A discriminative feature space for detecting and recognizing faces. In: Proc. Conf. Computer Vision and Pattern Recognition (CVPR 2004), vol. 2, pp. 797–804 (2004)Google Scholar
  5. 5.
    Shan, C., Gong, S., McOwan, P.W.: Robust facial expression recognition using local binary patterns. In: Proc. IEEE Int. Conf. on Image Processing (ICIP 2005), vol. II, pp. 914–917 (2005)Google Scholar
  6. 6.
    Yang, Z., Ai, H.: Demographic classification with local binary patterns. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 464–473. Springer, Heidelberg (2007)Google Scholar
  7. 7.
    Zhang, H., Gao, W., Chen, X., Zhao, D.: Object detection using spatial histogram features. Image Vision Comput. 24(4), 327–341 (2006)Google Scholar
  8. 8.
    Zhang, G., Huang, X., Li, S.Z., Wang, Y., Wu, X.: Boosting local binary pattern (LBP)-based face recognition. In: Li, S.Z., Lai, J.-H., Tan, T., Feng, G.-C., Wang, Y. (eds.) SINOBIOMETRICS 2004. LNCS, vol. 3338, pp. 179–186. Springer, Heidelberg (2004)Google Scholar
  9. 9.
    Rodriguez, Y., Marcel, S.: Face authentication using adapted local binary pattern histograms. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 321–332. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. In: Zhou, S.K., Zhao, W., Tang, X., Gong, S. (eds.) AMFG 2007. LNCS, vol. 4778, pp. 168–182. Springer, Heidelberg (2007)Google Scholar
  11. 11.
    Liao, S., Zhu, X., Lei, Z., Zhang, L., Li, S.Z.: Learning multi-scale block local binary patterns for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 828–837. Springer, Heidelberg (2007)Google Scholar
  12. 12.
    Chan, C.H., Kittler, J., Messer, K.: Multi-scale local binary pattern histograms for face recognition. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 809–818. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Yan, S., Wang, H., Tang, X., Huang, T.S.: Exploring feature descriptors for face recognition. In: Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP 2007) (2007)Google Scholar
  14. 14.
    Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local gabor binary pattern histogram sequence (LGBPHS): A novel non-statistical model for face representation and recognition. In: Proc. Tenth IEEE Int. Conf. on Computer Vision (ICCV 2005), vol. 1, pp. 786–791 (2005)Google Scholar
  15. 15.
    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)Google Scholar
  16. 16.
    Gauvain, J.L., Lee, C.H.: Maximum a posteriori estimation for multivariate gaussian mixture observations of markov chains. IEEE Trans. Speech and Audio Processing 2(2), 291–298 (1994)Google Scholar
  17. 17.
    Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as an image preprocessing for face authentication. In: Proc. IEEE Int. Conf. on Automatic Face and Gesture Recognition (FG 2006), pp. 9–14 (2006)Google Scholar
  18. 18.
    Scott, D.W.: Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley-Interscience, Hoboken (1992)Google Scholar
  19. 19.
    Vapnik, V.N.: Statistical Learning Theory. Wiley-Interscience, Hoboken (1998)Google Scholar
  20. 20.
    Ben-Yacoub, S., Abdeljaoued, Y., Mayoraz, E.: Fusion of face and speech data for person identity verification. IEEE Trans. Neural Networks 10(5), 1065–1074 (1999)Google Scholar
  21. 21.
    Heisele, B., Serre, T., Poggio, T.: A component-based framework for face detection and identification. Int. Journal of Computer Vision 74(2), 167–181 (2007)Google Scholar
  22. 22.
    Bailly-Bailliére, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariéthoz, J., Matas, J., Messer, K., Popovici, V., Porée, F., Ruíz, B., Thiran, J.P.: The BANCA database and evaluation protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 625–638. Springer, Heidelberg (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Timo Ahonen
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision GroupUniversity of OuluFinland

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