Illumination invariant representation for privacy preserving face identification

Boaz Moskovich, Margarita Osadchy

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Most effective face recognition methods store biometric information in the clear. Doing so exposes those systems to the risk of identity theft and violation of privacy. This problem significantly narrows the practical use of face recognition technology. Recent methods for privacy preserving face recognition address face verification task. Most of them are unable to generalize to unseen conditions and require a large number of images of every user for training. We address the problem of face identification, which is more useful in security applications, and propose a binary, illumination invariant representation that can be easily integrated with various efficient cryptographic tools for protection. We propose several privacy preserving applications for our representation and test it on a number of benchmark databases to show its robustness to severe illumination changes, occlusions, and some other appearance variations.

Original languageEnglish
Title of host publication2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Pages154-161
Number of pages8
DOIs
StatePublished - 2010
Event2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010 - San Francisco, CA, United States
Duration: 13 Jun 201018 Jun 2010

Publication series

Name2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010

Conference

Conference2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops, CVPRW 2010
Country/TerritoryUnited States
CitySan Francisco, CA
Period13/06/1018/06/10

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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