Viola-Jones Based Detectors: How Much Affects the Training Set?

  • Modesto Castrillón-Santana
  • Daniel Hernández-Sosa
  • Javier Lorenzo-Navarro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6669)

Abstract

This paper presents a study on the facial feature detection performance achieved using the Viola-Jones framework. A set of classifiers using two different focuses to gather the training samples is created and tested on four different datasets covering a wide range of possibilities. The results achieved should serve researchers to choose the classifier that better fits their demands.

Keywords

Viola-Jones detectors facial feature detection training sets 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Belhumeur, P., Hespanha, J., Kriegman, D.: Eigenfaces vs. Fisherfaces: Recognition using class specific linear projection. IEEE Trans. on PAMI 19(7), 711–720 (1997)CrossRefGoogle Scholar
  2. 2.
    Brubaker, S.C., Wu, J., Sun, J., Mullin, M.D., Rehg, J.M.: On the design of cascades of boosted ensembles for face detection. International Journal of Computer Vision 77, 65–86 (2008)CrossRefGoogle Scholar
  3. 3.
    Carnegie Mellon University: CMU/VACS image database: Frontal face images (1999), http://vasc.ri.cmu.edu/idb/html/face/frontal_images/index.html (last accesed May 11, 2007)
  4. 4.
    Castrillón, M., Déniz, O., Hernández, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the violajones general object detection framework. Machine Vision and Applications (2010) (in press)Google Scholar
  5. 5.
    Hjelmas, E., Low, B.K.: Face detection: A survey. Computer Vision and Image Understanding 83(3), 236–274 (2001), http://dx.doi.org/10.1006/cviu.2001.0921 CrossRefMATHGoogle Scholar
  6. 6.
    Huang, G.B., Ramesh, M., Berg, T., Learned-Miller, E.: Labeled faces in the wild: A database for studying face recognition in unconstrained environments. Tech. Rep. 07-49, University of Massachusetts, Amherst (October 2007)Google Scholar
  7. 7.
    Intel: Intel Open Source Computer Vision Library, v2.1 (April 2010), http://sourceforge.net/projects/opencvlibrary/ (last visited June 2010)
  8. 8.
    Jain, V., Learned-Miller., E.: Fddb: A benchmark for face detection in unconstrained settings. Tech. rep., University of Massachusetts, Amherst (2010)Google Scholar
  9. 9.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 90–95. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Lienhart, R., Maydt, J.: An extended set of Haar-like features for rapid object detection. In: IEEE ICIP 2002, vol. 1, pp. 900–903 (September 2002)Google Scholar
  11. 11.
    Reimondo, A.: Haar cascades repository (2007), http://alereimondo.no-ip.org/OpenCV/34 (last visited April 2010)
  12. 12.
    Rowley, H.A., Baluja, S., Kanade, T.: Neural network-based face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(1), 23–38 (1998)CrossRefGoogle Scholar
  13. 13.
    Schneiderman, H., Kanade, T.: A statistical method for 3d object detection applied to faces and cars. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1746–1759 (2000)Google Scholar
  14. 14.
    Seo, N.: Tutorial: OpenCV haartraining (rapid object detection with a cascade of boosted classifiers based on haar-like features), http://note.sonots.com/SciSoftware/haartraining.html (last visited June 2010)
  15. 15.
    Sung, K.K., Poggio, T.: Example-based learning for view-based human face detection. IEEE Trans. on Pattern Analysis and Machine Intelligence 20(1), 39–51 (1998)CrossRefGoogle Scholar
  16. 16.
    Viola, P., Jones, M.J.: Robust real-time face detection. International Journal of Computer Vision 57(2), 151–173 (2004)CrossRefGoogle Scholar
  17. 17.
    Yang, M.H., Kriegman, D., Ahuja, N.: Detecting faces in images: A survey. Transactions on Pattern Analysis and Machine Intelligence 24(1), 34–58 (2002), http://dx.doi.org/10.1109/34.982883 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Modesto Castrillón-Santana
    • 1
  • Daniel Hernández-Sosa
    • 1
  • Javier Lorenzo-Navarro
    • 1
  1. 1.SIANI Edif. Central del Parque Científico TecnológicoUniversidad de Las Palmas de Gran CanariaSpain

Personalised recommendations