Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications

Nalla, P R and C, Krishna Mohan (2015) Iris classification based on sparse representations using on-line dictionary learning for large-scale de-duplication applications. SpringerPlus, 4 (238). pp. 1-10. ISSN 2193-1801

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Abstract

De-duplication of biometrics is not scalable when the number of people to be enrolled into the biometric system runs into billions, while creating a unique identity for every person. In this paper, we propose an iris classification based on sparse representation of log-gabor wavelet features using on-line dictionary learning (ODL) for large-scale de-duplication applications. Three different iris classes based on iris fiber structures, namely, stream, flower, jewel and shaker, are used for faster retrieval of identities. Also, an iris adjudication process is illustrated by comparing the matched iris-pair images side-by-side to make the decision on the identification score using color coding. Iris classification and adjudication are included in iris de-duplication architecture to speed-up the identification process and to reduce the identification errors. The efficacy of the proposed classification approach is demonstrated on the standard iris database, UPOL.

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IITH Creators:
IITH CreatorsORCiD
C, Krishna MohanUNSPECIFIED
Item Type: Article
Uncontrolled Keywords: De-duplication; Biometrics; Iris fibers; Iris classification; Iris adjudication; Sparse representation; On-line dictionary learning
Subjects: Computer science > Big Data Analytics
Divisions: Department of Computer Science & Engineering
Depositing User: Team Library
Date Deposited: 22 Jun 2015 10:20
Last Modified: 01 Sep 2017 09:10
URI: http://raiithold.iith.ac.in/id/eprint/1590
Publisher URL: https://doi.org/10.1186/s40064-015-0971-1
OA policy: http://www.sherpa.ac.uk/romeo/issn/2193-1801/
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