Localizing parts of faces using a consensus of exemplars

  • Authors:
  • P. N. Belhumeur;D. W. Jacobs;D. J. Kriegman;N. Kumar

  • Affiliations:
  • -;-;-;-

  • Venue:
  • CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

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-the-art performance on the less challenging BioID dataset.