Face Detection — Efficient and Rank Deficient

  • Kienzle W
  • Franz M
  • Schölkopf B
 et al. 
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This paper proposes a method for computing fast approximations to sup- port vector decision functions in the field of object detection. In the present approach we are building on an existing algorithm where the set of support vectors is replaced by a smaller, so-called reduced set of syn- thesized input space points. In contrast to the existing method that finds the reduced set via unconstrained optimization, we impose a structural constraint on the synthetic points such that the resulting approximations can be evaluated via separable filters. For applications that require scan- ning large images, this decreases the computational complexity by a sig- nificant amount. Experimental results show that in face detection, rank deficient approximations are 4 to 6 times faster than unconstrained re- duced set systems.

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  • Wolf Kienzle

  • Matthias Franz

  • Bernhard Schölkopf

  • Gökhan Bakir

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