The Biometric Menagerie – A Fuzzy and Inconsistent Concept

Abstract

This paper proves that in iris recognition, the concepts of sheep, goats, lambs and wolves -as proposed by Doddington and Yager in the so-called Biometric Menagerie, are at most fuzzy and at least not quite well defined. They depend not only on the users or on their biometric templates, but also on the parameters that calibrate the iris recognition system. This paper shows that, in the case of iris recognition, the extensions of these concepts have very unsharp and unstable (non-stationary) boundaries. The membership of a user to these categories is more often expressed as a degree (as a fuzzy value) rather than as a crisp value. Moreover, they are defined by fuzzy Sugeno rules instead of classical (crisp) definitions. For these reasons, we said that the Biometric Menagerie proposed by Doddington and Yager could be at most a fuzzy concept of biometry, but even this status is conditioned by improving its definition. All of these facts are confirmed experimentally in a series of 12 exhaustive iris recognition tests undertaken for University of Bath Iris Image Database while using three different iris code dimensions (256x16, 128x8 and 64x4), two different iris texture encoders (Log-Gabor and Haar-Hilbert) and two different types of safety models.

Exploring Mobile Biometric Performance Through Identification of Core Factors and Relationships

“…The biometric menagerie is not without its critics Popescu-Bodorin et al [33] claim the concept is 'fuzzy' as to whether the categories are referring to the users themselves or the templates. Part of their claim highlights that the category of the users can change based upon the calibration of the system.…”

Fuzzy Membership, Possibility, Probability and Negation in Biometrics

“…By investigating the consistency of the concepts of "template ageing" and "biometric menagerie" [12], [23] we found an improved quinary recognition function and a quinary decisional model for iris biometrics, which is obtained while practicing iris recognition on intelligent iris verifier systems with stored digital identities [22]. In such systems, it is possible that the lowest scores are imposter scores, followed by a class of degraded imposter scores obtained by dishonest users when claiming (actively hunting) different identities than they actually have (hyena), followed by a class of uncertain scores centered in 0.5, and further to the right by a class of degraded genuine scores (goats).…”

“…Theoretically, in ideal conditions, it should be able to map all its legal inputs (pairs of iris templates) onto a set of two concepts and linguistic labels 'imposter' and 'genuine' whose extensions should be disjoint, since in a logically consistent iris recognition system and also in our reasoning, no imposter pair is a genuine one and vice versa. Unfortunately, the field of iris recognition is full of counter-examples to this ideal situation, some of them as old as the domain of iris recognition itself [3], [4] and others, very recent indeed [6]- [10], [24], [25], some of them in more direct connection with what follows to be presented in this paper [2], [12]- [17], [19]- [23]. In all of these counter-examples it happens that the linguistic labels 'imposter' and 'genuine' are, in fact, represented as two overlapping fuzzy sets of recognition scores, the overlapping being itself a fuzzy boundary in between the first two mentioned fuzzy sets.…”

A flexible approach for biometric menagerie on user classification of keystroke data

“…The biometric menagerie has been considered to be fuzzy and inconsistent for iris recognition, so that the fuzzy-linguistic labels of menagerie in terms of first/last wolf-, sheep-, lamb-, goat-templates, all claimed to depend on the calibration of the recognition system [7].…”