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Face Detection: Efficient and Rank Deficient


Conference Paper


This paper proposes a method for computing fast approximations to support 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 synthesized 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 scanning an entire image, this decreases the computational complexity of a scan by a significant amount. We present experimental results on a standard face detection database.

Author(s): Kienzle, W. and BakIr, G. and Franz, M. and Schölkopf, B.
Book Title: Advances in Neural Information Processing Systems 17
Journal: Advances in Neural Information Processing Systems
Pages: 673-680
Year: 2005
Month: July
Day: 0
Editors: LK Saul and Y Weiss and L Bottou
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 18th Annual Conference on Neural Information Processing Systems (NIPS 2004)
Event Place: Vancouver, BC, Canada

Address: Cambridge, MA, USA
Digital: 0
ISBN: 0-262-19534-8
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Face Detection: Efficient and Rank Deficient},
  author = {Kienzle, W. and BakIr, G. and Franz, M. and Sch{\"o}lkopf, B.},
  journal = {Advances in Neural Information Processing Systems},
  booktitle = {Advances in Neural Information Processing Systems 17},
  pages = {673-680},
  editors = {LK Saul and Y Weiss and L Bottou},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = jul,
  year = {2005},
  month_numeric = {7}