Face recognition with contiguous occlusion using Markov Random Fields

Zihan Zhou, Andrew Wagner, Hossein Mobahi, John Wright, Yi Ma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

140 Scopus citations

Abstract

Partially occluded faces are common in many applications of face recognition. While algorithms based on sparse representation have demonstrated promising results, they achieve their best performance on occlusions that are not spatially correlated (i.e. random pixel corruption). We show that such sparsity-based algorithms can be significantly improved by harnessing prior knowledge about the pixel error distribution. We show how a Markov Random Field model for spatial continuity of the occlusion can be integrated into the computation of a sparse representation of the test image with respect to the training images. Our algorithm ef-ficiently and reliably identifies the corrupted regions and excludes them from the sparse representation. Extensive experiments on both laboratory and real-world datasets show that our algorithm tolerates much larger fractions and varieties of occlusion than current state-of-the-art algorithms.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Pages1050-1057
Number of pages8
DOIs
StatePublished - Dec 1 2009
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: Sep 29 2009Oct 2 2009

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Conference

Conference12th International Conference on Computer Vision, ICCV 2009
CountryJapan
CityKyoto
Period9/29/0910/2/09

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

Fingerprint Dive into the research topics of 'Face recognition with contiguous occlusion using Markov Random Fields'. Together they form a unique fingerprint.

Cite this