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Biometric identification systems must be able to distin guish between individuals even in situations where the bio metric signature may be similar, such as in the case of iden tical twins. This paper presents experiments done in facial recognition using data from a set of images of twins. This work establishes the current state of facial recognition in regards to twins and the accuracy of current state-of-the art programs in distinguishing between identical twins us ing three commercial face matchers, Cognitec 8.3.2.0, Ver iLook 4.0, and PittPatt 4.2.1 and a baseline matcher em ploying Local Region PCA. Overall, it was observed that Cognitec had the best performance. All matchers, how ever, saw degradation in performance compared to an ex periment where the ability to distinguish unrelated persons was assessed. In particular, lighting and expression seemed to have affected performance the most.

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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.