A Geometric Approach to Face Detector Combining

  • Nikolay Degtyarev
  • Oleg Seredin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6713)

Abstract

In this paper, a method of combining face detectors is proposed, which is based on the geometry of the competing face detection results. The main idea of the method consists in finding groups of similar face detection results obtained by several algorithms and further averaging them. The combination result essentially depends on the number of algorithms that have fallen in each of the groups. The experimental evaluation of the method is based on seven algorithms: Viola-Jones (OpenCV 1.0), Luxand© FaceSDK, Face Detection Library, SIFinder, Algorithm of the University of Surrey, FaceOnIt, Neurotechnology© VeriLook. The paper contains practical results of their combination and a discussion of future improvements.

Keywords

combining classifiers face detection clustering of detector outputs combination of face detectors comparative test 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Beveridge, J.R., Alvarez, A., Saraf, J., et al.: Face Detection Algorithm and Feature Performance on FRGC 2.0 Imagery. In: Beveridge, J. (ed.) Proceedings of First International Conference on Biometrics, pp. 1–7. IEEE, Los Alamitos (2007)Google Scholar
  2. 2.
    Castrillon, M., Deniz, O., Hernandez, D., Lorenzo, J.: A comparison of face and facial feature detectors based on the Viola Jones general object detection framework. Machine Vision and Applications, pp. 1–14. Springer, Heidelberg (2010), doi:10.1007/s00138-010-0250-7Google Scholar
  3. 3.
    Degtyarev, N., Seredin, O.: Comparative Testing of Face Detection Algorithms. In: Elmoataz, A., Lezoray, O., Nouboud, F., Mammass, D., Meunier, J. (eds.) ICISP 2010. LNCS, vol. 6134, pp. 200–209. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Degtyarev, N., Seredin, O.: Effect of Eyes Detection and Position Estimation Methods on the Accuracy of Comparative Testing of Face Detection Algorithms. In: 10th International Conference on Pattern Recognition and Image Analysis: New Information Technologies (PRIA-10-2010), St. Petersburg, December 5-12, pp. 261–264. Politechnika (2010)Google Scholar
  5. 5.
    Duin, R.P.W., Tax, D.M.J.: Experiments with classifier combining rules. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 16–29. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  6. 6.
    Fawcett, T.: An introduction to ROC analysis. Pattern Recognition Letters 27(8), 861–874 (2006)CrossRefGoogle Scholar
  7. 7.
    Jesorsky, O., Kirchberg, K.J., Frischholz, R.W.: Robust face detection using the hausdorff distance. LNCS, pp. 90–95 (2001)Google Scholar
  8. 8.
    Jones, M.J.: Face Recognition: Where We Are and Where To Go From Here. IEEJ Trans. on Elect., Information and Systems 129(5), 770–777 (2009)CrossRefGoogle Scholar
  9. 9.
    Kienzle, W., Bakir, G., Franz, M., Scholkopf, B.: Face detection – efficient and rank deficient. Advan. in neural inform. process. systems 17, 673–680 (2005)Google Scholar
  10. 10.
    Krestinin, I.A., Seredin, O.S.: Excluding cascading classifier for face detection. In: Proc. of the 19th Int. Conf. on Computer Graphics and Vision, pp. 380–381 (2009)Google Scholar
  11. 11.
    Kittler, J.: Combining classifiers: A theoretical framework. Pattern Analysis & Applications 1(1), 18–27 (1998)CrossRefGoogle Scholar
  12. 12.
    Marcel, S., Keomany, J., et al.: Robust-to-illumination face localisation using Active Shape Models and Local Binary Patterns. In: IDIAP-RR, vol. 47 (2006)Google Scholar
  13. 13.
    Mohamed, N.M., Mahdi, H.: A simple evaluation of face detection algorithms using unpublished static images. In: Mohamed, N.M., Mahdi, H. (eds.) Proc. of 10th Intern. Conference on Intelligent Systems Design and Applications, pp. 1–5. IEEE Computer Society Press, Los Alamitos (2010), doi:10.1109/ISDA.2010.5687301Google Scholar
  14. 14.
    Viola, P., Jones, M.J.: Robust Real-Time Face Detection. International Journal of Computer Vision 57, 137–154 (2004)CrossRefGoogle Scholar
  15. 15.
    Wechsler, H.: Reliable face recognition methods: system design, implementation and evaluation, 329 p Springer, Heidelberg(2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Nikolay Degtyarev
    • 1
  • Oleg Seredin
    • 1
  1. 1.Tula State UniversityRussia

Personalised recommendations