Secure and robust iris recognition using random projections and sparse representations

Jaishanker K. Pillai, Vishal M. Patel, Rama Chellappa, Nalini K. Ratha

Research output: Contribution to journalArticlepeer-review

243 Scopus citations


Noncontact biometrics such as face and iris have additional benefits over contact-based biometrics such as fingerprint and hand geometry. However, three important challenges need to be addressed in a noncontact biometrics-based authentication system: ability to handle unconstrained acquisition, robust and accurate matching, and privacy enhancement without compromising security. In this paper, we propose a unified framework based on random projections and sparse representations, that can simultaneously address all three issues mentioned above in relation to iris biometrics. Our proposed quality measure can handle segmentation errors and a wide variety of possible artifacts during iris acquisition. We demonstrate how the proposed approach can be easily extended to handle alignment variations and recognition from iris videos, resulting in a robust and accurate system. The proposed approach includes enhancements to privacy and security by providing ways to create cancelable iris templates. Results on public data sets show significant benefits of the proposed approach.

Original languageEnglish (US)
Article number5719618
Pages (from-to)1877-1893
Number of pages17
JournalIEEE Transactions on Pattern Analysis and Machine Intelligence
Issue number9
StatePublished - 2011
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics


  • Iris recognition
  • cancelability
  • random projections
  • secure biometrics
  • sparse representations


Dive into the research topics of 'Secure and robust iris recognition using random projections and sparse representations'. Together they form a unique fingerprint.

Cite this