Empirical Studies of the Existence of the Biometric Menagerie in the FRGC 2.0 Color Image Corpus
CVPRW '06 Proceedings of the 2006 Conference on Computer Vision and Pattern Recognition Workshop
Validating a Biometric Authentication System: Sample Size Requirements
IEEE Transactions on Pattern Analysis and Machine Intelligence
Performance of Biometric Quality Measures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Wolf Attack Probability: A Theoretical Security Measure in Biometric Authentication Systems
IEICE - Transactions on Information and Systems
Incorporating Model-Specific Score Distribution in Speaker Verification Systems
IEEE Transactions on Audio, Speech, and Language Processing
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Improving biometric verification systems by fusing Z-norm and F-norm
CCBR'12 Proceedings of the 7th Chinese conference on Biometric Recognition
Assessing the level of difficulty of fingerprint datasets based on relative quality measures
Information Sciences: an International Journal
| Hi-index | 0.00 |
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.