On the consistency of the biometric menagerie for irises and iris matchers

The biometric menagerie is useful in identifying the troublesome users within a biometric recognition system. In order to maximize the benefits of the menagerie classifications, it is imperative that the classifications remain constant for each subject. Irises present one of the unique scenarios for classification since each iris represents the same subject but the two irises are independent of each other. We have taken the ICE 2005 iris image dataset [8] and applied three different iris recognition algorithms to it. For each algorithm, we classified the subjects within the biometric menagerie and studied the consistency of the classifications across algorithms. We also broke the dataset into subsets by left and right iris and studied the consistency of the classifications between irises. Our results have shown that the biometric menagerie classifications are algorithm dependent and dependent on which iris is chosen. One-third of the population was classified as a weak user by only a single algorithm and a quarter of the population had irises with non-matching classifications, one of which was a weak user classification. These two subsets of the population represent all the potentially weak users in the population but the subjects cannot be considered weak due to the disagreement between the algorithms and the mismatched classifications of the two irises. In order to use the biometric menagerie effectively, one algorithm must always be used for all recognitions and modalities must be kept in disjoint datasets to reliably label weak users.