IEEE Trans Pattern Anal Mach Intell - Secure and Robust Iris Recognition Using Random Projections and Sparse Representations.

Tópicos

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Resumo

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.

Resumo Limpo

noncontact biometr face iri addit benefit contactbas biometr fingerprint hand geometri howev three import challeng need address noncontact biometricsbas authent system abil handl unconstrain acquisit robust accur match privaci enhanc without compromis secur paper propos unifi framework base random project spars represent can simultan address three issu mention relat iri biometr propos qualiti measur can handl segment error wide varieti possibl artifact iri acquisit demonstr propos approach can easili extend handl align variat recognit iri video result robust accur system propos approach includ enhanc privaci secur provid way creat cancel iri templat result public data set show signific benefit propos approach

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