Video-Based Face Verification with Local Binary Patterns and SVM Using GMM Supervectors

  • Tiago F. Pereira
  • Marcus A. Angeloni
  • Flávio O. Simões
  • José Eduardo C. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7333)


The classification task has a relevant importance in face verification systems and there are many approaches proposed to solve it. This paper shows a new approach for the classification task in video-based face verification systems using Support Vector Machines (SVM) as classifier and Gaussian Mixture Models (GMM) working as its kernel. The use of Local Binary Patterns (LBP) for face description, in conjunction with the generation of Gaussian supervectors as input points for the classifier, describes the temporal information contained in a video by a unique feature point, which seems to be a very compact and powerful form of representation. Our experimental results, performed on MOBIO database and protocol, shows the advantages of the proposed technique.


Face Verification Local Binary Patterns Support Vector Machines Gaussian Mixture Models 


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  1. 1.
    Ahonen, T., Hadid, A., Pietikäinen, M.: Face Recognition with Local Binary Patterns. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face Description with Local Binary Patterns: Application to Face Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2037–2041 (2006)CrossRefGoogle Scholar
  3. 3.
    Bredin, H., Dehak, N., Chollet, G.: GMM-based SVM for face recognition. In: International Conference on Pattern Recognition, vol. (3), pp. 1111–1114 (2006)Google Scholar
  4. 4.
    Campbell, W., Sturim, D., Reynolds, D.: Support vector machines using GMM supervectors for speaker verification. IEEE Signal Processing Letters 13, 308–311 (2006)CrossRefGoogle Scholar
  5. 5.
    Cardinaux, F., Sanderson, C., Bengio, S.: Face verification using adapted generative models. In: IEEE International Conference on Automatic Face and Gesture Recognition, pp. 825–830 (2004)Google Scholar
  6. 6.
    Chan, C.: Multi-scale Local Binary Pattern Histogram for Face Recognition. PhD thesis - University of Surrey (2008)Google Scholar
  7. 7.
    Dempster, A., Laird, N., Rubin, D.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 1–38 (1977)Google Scholar
  8. 8.
    Li, S., Jain, A.: Handbook of Face Recognition, 2nd edn. Springer (2011)Google Scholar
  9. 9.
    Linde, Y., Buzo, A., Gray, R.: An algorithm for vector quantizer design. IEEE Transactions on Communications 28, 84–95 (1980)CrossRefGoogle Scholar
  10. 10.
    Luo, Y., Gavrilova, M.: 3D Facial Model Synthesis using Voronoi Approach. In: 3rd International Symposium on Voronoi Diagrams in Science and Engineering, pp. 132–137 (2006)Google Scholar
  11. 11.
    Marcel, S., McCool, C., Matejka, P., Ahonen, T., Cernocky, J., Chakraborty, S., Balasubramanian, V., Panchanathan, S., Chan, C., Kittler, J., Poh, N., Fauve, B., Glembek, O., Plchot, O., Jancik, Z., Larcher, A., Levy, C., Matrouf, D., Bonastre, J., Lee, P., Hung, J., Wu, S., Hung, Y., Machlica, L., Mason, J., Mau, S., Sanderson, C., Monzo, D., Albiol, A., Nguyen, H., Bai, L., Wang, Y., Niskanen, M., Turtinen, M., Nolazco-Flores, J., Garcia-Perera, L., Aceves-Lopez, R., Villegas, M., Paredes, R.: On the results of the first mobile biometry (MOBIO) face and speaker verification evaluation. In: International Conference on Pattern Recognition, pp. 210–225 (2010)Google Scholar
  12. 12.
    McCool, C., Marcel, S.: MOBIO database for the ICPR 2010 face and speech competition. Idiap Research Institute, Technical Report (2009)Google Scholar
  13. 13.
    Ojala, T., Pietikainen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recognition 29, 51–59 (1996)CrossRefGoogle Scholar
  14. 14.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 971–987 (2002)Google Scholar
  15. 15.
    Reynolds, D., Quatieri, T., Dunn, R.: Speaker verification using adapted Gaussian mixture models. Digital Signal Processing 10, 19–41 (2000)CrossRefGoogle Scholar
  16. 16.
    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)CrossRefGoogle Scholar
  17. 17.
    Tan, P., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining. Pearson Addison Wesley, Boston (2006)Google Scholar
  18. 18.
    Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Transactions on Image Processing 19, 1635–1650 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Turk, M., Pentland, A.: Eigenfaces for recognition. Journal of Cognitive Neuroscience 3, 71–86 (1991)CrossRefGoogle Scholar
  20. 20.
    Viola, P., Jones, M.: Robust real-time face detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  21. 21.
    Zhao, W., Chellappa, R., Phillips, P., Rosenfeld, A.: Face recognition: A literature survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Tiago F. Pereira
    • 1
  • Marcus A. Angeloni
    • 1
  • Flávio O. Simões
    • 1
  • José Eduardo C. Silva
    • 1
  1. 1.CPqD – Research and Development Center in TelecommunicationsCampinasBrazil

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