Sparse representations and random projections for robust and cancelable biometrics

Vishal M. Patel, Rama Chellappa, Massimo Tistarelli

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

Abstract

In recent years, the theories of Sparse Representation (SR) and Compressed Sensing (CS) have emerged as powerful tools for efficiently processing data in non-traditional ways. An area of promise for these theories is biometric identification. In this paper, we review the role of sparse representation and CS for efficient biometric identification. Algorithms to perform identification from face and iris data are reviewed. By applying Random Projections it is possible to purposively hide the biometric data within a template. This procedure can be effectively employed for securing and protecting personal biometric data against theft. Some of the most compelling challenges and issues that confront research in biometrics using sparse representations and CS are also addressed.

Original languageEnglish (US)
Title of host publication11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
Pages1-6
Number of pages6
DOIs
StatePublished - 2010
Externally publishedYes
Event11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010 - Singapore, Singapore
Duration: Dec 7 2010Dec 10 2010

Publication series

Name11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010

Other

Other11th International Conference on Control, Automation, Robotics and Vision, ICARCV 2010
Country/TerritorySingapore
CitySingapore
Period12/7/1012/10/10

Keywords

  • Cancelable biometrics
  • Face recognition
  • Iris recognition
  • Random projections
  • Sparse representations

ASJC Scopus subject areas

  • Artificial Intelligence
  • Control and Systems Engineering

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