  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.
