CSDL Home IEEE Transactions on Pattern Analysis & Machine Intelligence 2011 vol.33 Issue No.09 - September
Issue No.09 - September (2011 vol.33)
Jaishanker K. Pillai , University of Maryland, College Park
Vishal M. Patel , University of Maryland, College Park
Rama Chellappa , University of Maryland, College Park
Nalini K. Ratha , IBM T.J. Watson Research Center, Hawthorne
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2011.34
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.
Iris recognition, cancelability, secure biometrics, random projections, sparse representations.
Jaishanker K. Pillai, Vishal M. Patel, Rama Chellappa, Nalini K. Ratha, "Secure and Robust Iris Recognition Using Random Projections and Sparse Representations", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.33, no. 9, pp. 1877-1893, September 2011, doi:10.1109/TPAMI.2011.34