• Documents
  • Authors
  • Tables
  • Log in
  • Sign up
  • MetaCart
  • DMCA
  • Donate

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations

DMCA

Kriegman-Belhumeur Vision Technologies

Cached

  • Download as a PDF

Download Links

  • [www.cs.washington.edu]
  • [www.cs.columbia.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [neerajkumar.org]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [homes.cs.washington.edu]
  • [neerajkumar.org]
  • [neerajkumar.org]
  • [neerajkumar.org]
  • [neerajkumar.org]
  • [neerajkumar.org]

  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by Peter N. Belhumeur , David W. Jacobs , David J. Kriegman , Neeraj Kumar
  • Summary
  • Citations
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Belhumeur_kriegman-belhumeurvision,
    author = {Peter N. Belhumeur and David W. Jacobs and David J. Kriegman and Neeraj Kumar},
    title = {Kriegman-Belhumeur Vision Technologies},
    year = {}
}

Share

Facebook Twitter Reddit Bibsonomy

OpenURL

 

Abstract

We present a novel approach to localizing parts in images of human faces. The approach combines the output of local detectors with a non-parametric set of global models for the part locations based on over one thousand hand-labeled exemplar images. By assuming that the global models generate the part locations as hidden variables, we derive a Bayesian objective function. This function is optimized using a consensus of models for these hidden variables. The resulting localizer handles a much wider range of expression, pose, lighting and occlusion than prior ones. We show excellent performance on a new dataset gathered from the internet and show that our localizer achieves state-of-theart performance on the less challenging BioID dataset. 1.

Powered by: Apache Solr
  • About CiteSeerX
  • Submit and Index Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2019 The Pennsylvania State University