Advertisement

Distance Measures for Gabor Jets-Based Face Authentication: A Comparative Evaluation

  • Daniel González-Jiménez
  • Manuele Bicego
  • J. W. H. Tangelder
  • B. A. M Schouten
  • Onkar Ambekar
  • José Luis Alba-Castro
  • Enrico Grosso
  • Massimo Tistarelli
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

Local Gabor features (jets) have been widely used in face recognition systems. Once the sets of jets have been extracted from the two faces to be compared, a proper measure of similarity (or distance) between corresponding features should be chosen. For instance, in the well known Elastic Bunch Graph Matching (EBGM) approach and other Gabor-based face recognition systems, the cosine distance was used as a measure. In this paper, we provide an empirical evaluation of seven distance measures for comparison, using a recently introduced face recognition system, based on Shape Driven Gabor Jets (SDGJ). Moreover we evaluate different normalization factors that are used to pre-process the jets. Experimental results on the BANCA database suggest that the concrete type of normalization applied to jets is a critical factor, and that some combinations of normalization + distance achieve better performance than the classical cosine measure for jet comparison.

Keywords

Face Recognition Local Binary Pattern Manhattan Distance Gabor Feature Face Recognition System 
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.
    Wiskott, L., Fellous, J.M., Kruger, N., von der Malsburg, C.: Face Recognition by Elastic Bunch Graph Matching. IEEE Transactions on PAMI 19(7), 775–779 (1997)Google Scholar
  2. 2.
    Duc, B., Fischer, S., Bigun, J.: Face Authentication with Gabor Information on Deformable Graphs. IEEE Transactions on Image Processing 8(4), 504–516 (1999)CrossRefGoogle Scholar
  3. 3.
    Smeraldi, F., Bigun, J.: Retinal Vision applied to Facial Features Detection and Face Authentication. Pattern Recognition Letters 23(4), 463–475 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    González-Jiménez, D., Alba-Castro, J.L.: Shape Contexts and Gabor Features for Face Description and Authentication. In: Proceedings IEEE ICIP 2005, pp. 962–965 (2005)Google Scholar
  5. 5.
    Shen, L., Bai, L.: A review on Gabor wavelets for Face Recognition. Pattern Analysis and Applications 9, 273–292 (2006)CrossRefMathSciNetGoogle Scholar
  6. 6.
    López, A.M., Lumbreras, F., Serrat, J., Villanueva, J.J.: Evaluation of Methods for Ridge and Valley Detection. IEEE Transactions on PAMI 21(4), 327–335 (1999)Google Scholar
  7. 7.
    Belongie, S., Malik, J., Puzicha, J.: Shape Matching and Object Recognition Using Shape Contexts. IEEE Transactions on PAMI 24(24), 509–522 (2002)Google Scholar
  8. 8.
    Bailly-Bailliere, E., et al.: The BANCA Database and Evaluation Protocol. In: Kittler, J., Nixon, M.S. (eds.) AVBPA 2003. LNCS, vol. 2688, pp. 625–638. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Messer, K., et al.: Face Authentication Test on the BANCA Database. In: Zhang, D., Jain, A.K. (eds.) ICBA 2004. LNCS, vol. 3072, pp. 8–15. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Bicego, M., Lagorio, A., Grosso, E., Tistarelli, M.: On the Use of SIFT Features for Face Authentication. In: Proc. of IEEE CVPR Workshop on Biometrics, p. 35 (2006)Google Scholar
  11. 11.
    Kohir, V.V., Desai, U.B.: Face Recognition Using DCT-HMM Approach. In: AFIART. Proc. Workshop on Advances in Facial Image Analysis and Recognition Technology, Freiburg, Germany (1998)Google Scholar
  12. 12.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int. Journal of Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  13. 13.
    Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: Application to face recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 28(12), 2037–2041Google Scholar
  14. 14.
    Jiao, F., Gao, W., Shan, S.: A Face Recognition Method Based on Local Feature Analysis. In: ACCV 2002, Melbourne, Australia, pp. 188–192 (2002)Google Scholar
  15. 15.
    Zhao, W., Chellappa, R., Phillips, P.J., Rosenfeld, A.: Face Recognition: A Literature Survey. ACM Computing Surveys 35, 399–458 (2003)CrossRefGoogle Scholar
  16. 16.
    González-Jiménez, D., Pérez-González, F., Comesaña-Alfaro, P., Pérez-Freire, L., Alba-Castro, J.L.: Modeling Gabor Coefficients Via Generalized Gaussian Distributions for Face Recognition. In: IEEE International Conference on Image Processing 2007 (accepted)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Daniel González-Jiménez
    • 1
  • Manuele Bicego
    • 2
  • J. W. H. Tangelder
    • 3
  • B. A. M Schouten
    • 3
  • Onkar Ambekar
    • 3
  • José Luis Alba-Castro
    • 1
  • Enrico Grosso
    • 2
  • Massimo Tistarelli
    • 4
  1. 1.TSC Department, University of Vigo, VigoSpain)
  2. 2.DEIR - University of Sassari, SassariItaly)
  3. 3.CWI, AmsterdamThe Netherlands)
  4. 4.DAP - University of Sassari, AlgheroItaly)

Personalised recommendations