A Nonparametric Approach to Face Detection Using Ranklets

  • Fabrizio Smeraldi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2688)


Ranklets are multiscale, orientation-selective, nonparametric rank features similar to Haar wavelets, suitable for characterising complex patterns. In this work, we employ a vector of ranklets to encode the appearance of an image frame representing a potential face candidate. Classification is based on density estimation by means of regularised histograms. Our procedure outperforms SNoW, linear and polynomial SVMs (based on independently published results) in face detection experiments over the 24’045 test images in the MIT-CBCL database.


Face Detection Nonparametric Approach Haar Wavelet Univariate Density Equal Error Rate 
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]
    Vapnik, V.N.: The nature of statistical learning theory. Springer-Verlag (1995)Google Scholar
  2. [2]
    Osuna, E., Freund, R., Girosi, F.: Training Support Vector Machines: an application to face detection. In: Proceedings of CVPR’ 97. (1997)Google Scholar
  3. [3]
    Schneiderman, H., Kanade, T.: A statistical method for 3D object recognition applied to faces and cars. In: Proceedings of CVPR, IEEE (2000) 746–751Google Scholar
  4. [4]
    Lehmann, E. L.: Nonparametrics: Statistical methods based on ranks. Holden-Day (1975)Google Scholar
  5. [5]
    Bhat, D. N., Nayar, S.K.: Ordinal measures for visual correspondence. In: Proceedings of CVPR. (1996) 351–357Google Scholar
  6. [6]
    Kendall, M., Gibbons, J.D.: Rank correlation methods. Edward Arnold (1990)Google Scholar
  7. [7]
    Zabih, R., Woodfill, J.: Non-parametric local transforms for computing visual correspondence. In: Proceedings of the 3rd ECCV. (1994) 151–158Google Scholar
  8. [8]
    Smeraldi, F.: Ranklets: orientation selective non-parametric features applied to face detection. In: Proc. of the 16th ICPR, Quebec, CA. Volume 3. (2002) 379–382Google Scholar
  9. [9]
    Daubechies, I.: Ten lectures in wavelets. SIAM, Philadelphia, USA (1992)Google Scholar
  10. [11]
    Alvira, M., Rifkin, R.: An empirical comparison of SNoW and SVMs for face detection. Technical Report AI Memo 2001-004-CBCL Memo 193, MIT (2001)

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Fabrizio Smeraldi
    • 1
  1. 1.Queen MaryUniversity of LondonLondonUK

Personalised recommendations