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Efficient face detection by a cascaded support-vector machine expansion




We describe a fast system for the detection and localization of human faces in images using a nonlinear ‘support-vector machine‘. We approximate the decision surface in terms of a reduced set of expansion vectors and propose a cascaded evaluation which has the property that the full support-vector expansion is only evaluated on the face-like parts of the image, while the largest part of typical images is classified using a single expansion vector (a simpler and more efficient classifier). As a result, only three reduced-set vectors are used, on average, to classify an image patch. Hence, the cascaded evaluation, presented in this paper, offers a thirtyfold speed-up over an evaluation using the full set of reduced-set vectors, which is itself already thirty times faster than classification using all the support vectors.

Author(s): Romdhani, S. and Torr, P. and Schölkopf, B. and Blake, A.
Journal: Proceedings of The Royal Society of London A
Volume: 460
Number (issue): 2501
Pages: 3283-3297
Year: 2004
Month: November
Day: 0
Series: A

Department(s): Empirical Inference
Bibtex Type: Article (article)

DOI: 10.1098/rspa.2004.1333
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Efficient face detection by a cascaded support-vector machine expansion},
  author = {Romdhani, S. and Torr, P. and Sch{\"o}lkopf, B. and Blake, A.},
  journal = {Proceedings of The Royal Society of London A},
  volume = {460},
  number = {2501},
  pages = {3283-3297},
  series = {A},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = nov,
  year = {2004},
  month_numeric = {11}