http://iet.metastore.ingenta.com
1887

Searching for doppelgängers: assessing the universality of the IrisCode impostors distribution

Searching for doppelgängers: assessing the universality of the IrisCode impostors distribution

For access to this article, please select a purchase option:

Buy article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Biometrics — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

The authors generated 316,250 entire distributions of IrisCode impostor scores, each distribution obtained by comparing one iris against hundreds of thousands of others in a database including persons spanning 152 nationalities. Altogether 100 billion iris comparisons were performed in this study. The purpose was to evaluate whether, in the tradition of Doddington's Zoo, some individuals are inherently more prone than most to generate iris false matches, while others are inherently less prone. With the standard score normalisation disabled, a detailed inter-quantile analysis showed that meaningful deviations from a universal impostors distribution occur only for individual distributions that are highly extreme in both their mean and their standard deviation, and which appear to make up <1% of the population. In general, when different persons are compared, the IrisCode produces relatively constant dissimilarity distances having an invariant narrow distribution, thanks to the large entropy which lies at the heart of this biometric modality. The authors discuss the implications of these findings and their caveats for various search strategies, including ‘1-to-first’ and ‘1-to-many’ iris matching.

References

    1. 1)
      • 1. http://www.CL.cam.ac.uk/users/jgd1000/Doppelganger-photos.pdf.
        .
    2. 2)
      • 2. https://portal.uidai.gov.in/uidwebportal/dashboard.do, Indian Government dashboard showing enrollment progress of the Unique IDentification Authority of India, updated weekly..
        .
    3. 3)
    4. 4)
    5. 5)
    6. 6)
      • G. Doddington , W. Liggett , A. Martin .
        6. Doddington, G., Liggett, W., Martin, A., et al: ‘Sheep, goats, lambs and wolves: a statistical analysis of speaker performance in the NIST 1998 speaker recognition evaluation’. Proc. of Int. Conf. on Spoken Language Processing, 1998.
        . Proc. of Int. Conf. on Spoken Language Processing
    7. 7)
    8. 8)
      • 8. http://www.CL.cam.ac.uk/users/jgd1000/SupplementaryGraphsDoppel.pdf.
        .
    9. 9)
      • P. Grother , G.W. Quinn , J.R. Matey . (2012)
        9. Grother, P., Quinn, G.W., Matey, J.R., et al: ‘IREX-III: performance of iris identification algorithms’. NIST Interagency Report 7836, NIST, Gaithersburg, MD, April 6, 2012.
        .
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-bmt.2015.0071
Loading

Related content

content/journals/10.1049/iet-bmt.2015.0071
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address