Face Description for Perceptual User Interfaces

  • M. Castrillón-Santana
  • J. Lorenzo-Navarro
  • D. Hernández-Sosa
  • J. Isern-González
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4177)


We investigate mechanisms which can endow the computer with the ability of describing a human face by means of computer vision techniques. This is a necessary requirement in order to develop HCI approaches which make the user feel himself/herself perceived. This paper describes our experiences considering gender, race and the presence of moustache and glasses. This is accomplished comparing, on a set of 6000 facial images, two different face representation approaches: Principal Components Analysis (PCA) and Gabor filters. The results achieved using a Support Vector Machine (SVM) based classifier are promising and particularly better for the second representation approach.


Support Vector Machine Face Recognition Gabor Filter Facial Expression Recognition Gabor Feature 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Argyle, M.: Bodily communication. Methuen, 2nd edn. (1988)Google Scholar
  2. 2.
    Bailly-Bailliere, E., Bengio, S., Bimbot, F., Hamouz, M., Kittler, J., Mariethoz, J., Matas, J., Messer, K., Popovici, V., Poree, F., Ruiz, B., Thiran, J.-P.: The banca database and evaluation protocol. In: Kittler, J., Nixon, M. (eds.) Proc. Audio- and Video-Based Biometric Person Authentication, Berlin, pp. 625–638. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  3. 3.
    Bartlett, M.S., Littlewort, G., Fasel, I., Movellan, J.R.: Real time face detection and facial expression recognition: Development and applications to human computer interaction. In: Computer Vision and Pattern Recognition (2003)Google Scholar
  4. 4.
    Belhumeur, P.N., Hespanha, J.P., Kriegman, D.J.: Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)Google Scholar
  5. 5.
    Bolme, D.: Elastic bunch graph matching. Master’s thesis, Colorado State University, Computer Science Department (June 2003)Google Scholar
  6. 6.
    Bruce, V., Young, A.: The eye of the beholder. Oxford University Press, Oxford (1998)Google Scholar
  7. 7.
    Campbell, N.W., Thomas, B.T.: Automatic selection of gabor filters for pixel classification. In: Sixth International Conference on Image Processing and its Applications, pp. 761–765 (July 1997)Google Scholar
  8. 8.
    Castrillón Santana, M., Lorenzo Navarro, J., Hernández Sosa, D., Rodríguez-Domínguez, Y.: An analysis of facial description in static images and video streams. In: 2nd Iberian Conference on Pattern Recognition and Image Analysis, Estoril, Portugal (June 2005)Google Scholar
  9. 9.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001),
  10. 10.
    Daugman, J.G.: Complete discrete 2-d gabor transforms by neural networks for image analysis and compression. IEEE Trans. on Acoustics, Speech, and Signal Processing 36(7) (1988)Google Scholar
  11. 11.
    Frischholz, R.W., Dieckmann, U.: Bioid: A multimodal biometric identification system. IEEE Computer 33(2) (2000)Google Scholar
  12. 12.
    Gokberk, B., Akarun, L., Alpaydýn, E.: Feature selection for pose invariant face recognition. In: International Conference on Pattern Recognition, Barcelona, Spain (2002)Google Scholar
  13. 13.
    Gosselin, F., Schyns, P.G.: Bubbles: a technique to reveal the use of information in recognition tasks. Vision Research, pp. 2261–2271 (2001)Google Scholar
  14. 14.
    Intel. Intel Open Source Computer Vision Library, b4.0 (August. 2004),
  15. 15.
    Jing, Z., Mariani, R.: Glasses detection and extraction by deformable contour. In: International Conference on Pattern Recognition (2000)Google Scholar
  16. 16.
    Jones, J.P., Palmer, L.A.: An evaluation of the two-dimensional gabor filter model of simple receptive fields in cat striate cortex. Journal of Neurophisiology 58(6), 1233–1258 (1987)Google Scholar
  17. 17.
    Kirby, Y., Sirovich, L.: Application of the karhunen-loève procedure for the characterization of human faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(1) (1990)Google Scholar
  18. 18.
    Lyons, M.J., Budyneck, J., Akamatsu, S.: Automatic classification of single facial images. IEEE Transactions on Pattern Analysis and Machine Intelligence 21(12), 1357–1362 (1999)CrossRefGoogle Scholar
  19. 19.
    Moghaddam, B., Yang, M.-H.: Learning gender with support faces. IEEE Trans. on Pattern Analysis and Machine Intelligence 24(5), 707–711 (2002)CrossRefGoogle Scholar
  20. 20.
    Olshausen, B.A., Field, D.J.: Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature 381, 607–609 (1996)CrossRefGoogle Scholar
  21. 21.
    Pantic, M., Rothkrantz, L.J.M.: Automatic analysis of facial expressions: The state of the art. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12), 1424–1445 (2000)CrossRefGoogle Scholar
  22. 22.
    Phillips, P.J., Moon, H., Rizvi, S.A., Rauss, P.J.: The feret evaluation methodology for face recognition algorithms. TR 6264, NISTIR (January 1999)Google Scholar
  23. 23.
    Pollen, D.A., Ronner, S.F.: Phase relationship between adjacent simple cells in the visual cortex. Science 212, 1409–1411 (1981)CrossRefGoogle Scholar
  24. 24.
    Sinha, P., Torralba, T.P.: I think i know that face. Nature 384(6608), 384–404 (1996)Google Scholar
  25. 25.
    Sun, Z., Bebis, G., Yuan, X., Louis, S.J.: Genetic feature subset selection for gender classification: A comparison study. In: Sixth IEEE Workshop on Applications of Computer Vision (December 2002)Google Scholar
  26. 26.
    Torralba, A.: Contextual modulation of target saliency. Advances in Neural Information Processing Systems (2001)Google Scholar
  27. 27.
    Turk, M.: Computer vision in the interface. Communications of the ACM 47(1), 61–67 (2004)CrossRefGoogle Scholar
  28. 28.
    Vapnik, V.: The nature of statistical learning theory. Springer, New York (1995)zbMATHGoogle Scholar
  29. 29.
    Wu, B., Ai, H., Liu, R.: Glasses detection by boosting simple wavelet features. In: 17th Int. Conf. on Pattern Recognition, Cambridge, UK, pp. 292–295 (August 2004)Google Scholar
  30. 30.
    Yang, P., Shan, S., Gao, W., Li, S.Z., Zhang, D.: Face recognition using ada-boosted gabor features. In: Proc. of the 6th International Conference on Automatic Face and Gesture Recognition (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • M. Castrillón-Santana
    • 1
  • J. Lorenzo-Navarro
    • 1
  • D. Hernández-Sosa
    • 1
  • J. Isern-González
    • 1
  1. 1.IUSIANI, Edificio Central del Parque Científico-Tecnológico, Campus Universitario de TafiraUniversidad de Las Palmas de Gran CanariaLas PalmasSpain

Personalised recommendations