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A Biometric Menagerie Index for Characterising Template/Model-Specific Variation

  • Norman Poh
  • Josef Kittler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5558)

Abstract

An important phenomenon influencing the performance of a biometric experiment, attributed to Doddington et al (1998), is that the match scores (whether under genuine or impostor matching) are strongly dependent on the model or template from which the match scores have been derived. Although there exist studies to classify the characteristic of the template/model, as well as the query data, into animal names such as sheep, goats, wolves and lambs – so-called Doddington’s menagerie, or higher semantic categories considering simultaneously both genuine and impostor match scores, due to Yager and Dunstone (2008), there is currently absence of means to characterise the extent of Doddington’s menagerie. This paper aims to design such an index, called the biometric menagerie index (BMI). It is defined as the ratio of the between-client variance and the expectation of the total variance. BMI has three desirable properties. First, it is invariant to shifting and scaling of the match scores. Second, its value lies between zero and one, with zero implying the absence of Doddington’s menagerie effect, and one signifying its strong presence. Third, it is experimentally verified that BMI generalizes to different choices of impostor population. Our findings based on the XM2VTS benchmark score database suggest the followings: First, the BMI of genuine match scores is generally higher than that of the impostor match scores. Second, two different matching algorithms observing the same biometric data may have significantly different BMI values, hence suggesting that the biometric menagerie is algorithm-dependent.

Keywords

Biometric System Biometric Authentication Gaussian Copula Biometric Modality Biometric Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Norman Poh
    • 1
  • Josef Kittler
    • 1
  1. 1.CVSSPUniversity of Surrey, GuildfordSurreyUK

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