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Face Recognition with Local Binary Patterns

  • Timo Ahonen
  • Abdenour Hadid
  • Matti Pietikäinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3021)

Abstract

In this work, we present a novel approach to face recognition which considers both shape and texture information to represent face images. The face area is first divided into small regions from which Local Binary Pattern (LBP) histograms are extracted and concatenated into a single, spatially enhanced feature histogram efficiently representing the face image. The recognition is performed using a nearest neighbour classifier in the computed feature space with Chi square as a dissimilarity measure. Extensive experiments clearly show the superiority of the proposed scheme over all considered methods (PCA, Bayesian Intra/extrapersonal Classifier and Elastic Bunch Graph Matching) on FERET tests which include testing the robustness of the method against different facial expressions, lighting and aging of the subjects. In addition to its efficiency, the simplicity of the proposed method allows for very fast feature extraction.

Keywords

Face Recognition Linear Discriminant Analysis Recognition Rate Face Image Local Binary Pattern 
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.
    Phillips, P., Grother, P., Micheals, R.J., Blackburn, D.M., Tabassi, E., Bone, J.M.: Face recognition vendor test 2002 results. Technical report (2003)Google Scholar
  2. 2.
    Zhao, W., Chellappa, R., Rosenfeld, A., Phillips, P.J.: Face recognition: a literature survey. Technical Report CAR-TR-948, Center for Automation Research, University of Maryland (2002)Google Scholar
  3. 3.
    Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image and Vision Computing 16, 295–306 (1998)CrossRefGoogle Scholar
  4. 4.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  5. 5.
    Etemad, K., Chellappa, R.: Discriminant analysis for recognition of human face images. Journal of the Optical Society of America 14, 1724–1733 (1997)CrossRefGoogle Scholar
  6. 6.
    Wiskott, L., Fellous, J.M., Kuiger, N., von der Malsburg, C.: Face recognition by elastic bunch graph matching. IEEE Transaction on Pattern Analysis and Machine Intelligence 19, 775–779 (1997)CrossRefGoogle Scholar
  7. 7.
    Moghaddam, B., Nastar, C., Pentland, A.: A bayesian similarity measure for direct image matching. In: 13th International Conference on Pattern Recognition, pp. II: 350–358 (1996)Google Scholar
  8. 8.
    Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefGoogle Scholar
  9. 9.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on feature distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  10. 10.
    Gong, S., McKenna, S.J., Psarrou, A.: Dynamic Vision, From Images to Face Recognition. Imperial College Press, London (2000)Google Scholar
  11. 11.
    Bolme, D.S., Beveridge, J.R., Teixeira, M., Draper, B.A.: The CSU face identification evaluation system: Its purpose, features and structure. In: Third International Conference on Computer Vision Systems, pp. 304–311 (2003)Google Scholar
  12. 12.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The FERET evaluation methodology for face recognition algorithms. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1090–1104 (2000)CrossRefGoogle Scholar
  13. 13.
    Beveridge, J.R., She, K., Draper, B.A., Givens, G.H.: A nonparametric statistical comparison of principal component and linear discriminant subspaces for face recognition. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. I: 535–542 (2001)Google Scholar
  14. 14.
    Samaria, F.S., Harter, A.C.: Parameterisation of a stochastic model for human face identification. In: IEEE Workshop on Applications of Computer Vision, pp. 138–142 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Timo Ahonen
    • 1
  • Abdenour Hadid
    • 1
  • Matti Pietikäinen
    • 1
  1. 1.Machine Vision Group,Infotech OuluUniversity of OuluFinland

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