In Biometrics, facial uniqueness is commonly inferred from impostor similarity scores. In this paper, we show that such uniqueness measures are highly unstable in the presence of image quality variations like pose, noise and blur. We also experimentally demonstrate the instability of a recently introduced impostor-based uniqueness measure of [Klare and Jain 2013] when subject to poor quality facial images.
Impostor score distribution has been widely used to identify the subjects that exhibit high level of similarity to other subjects in a population (i.e. lamb). The authors of @cite_15 investigated the existence of lamb'' in speech data by analyzing the relative difference between maximum impostor score and genuine score of a subject. They expected the lambs'' to have very high maximum impostor score. A similar strategy was applied by @cite_14 to locate non-unique faces in a facial image dataset. The authors of @cite_13 tag a subject as lamb'' if its mean impostor score lies above a certain threshold. Based on this knowledge of a subject's location in the Doddington zoo'' @cite_15 , they propose an adaptive fusion scheme for a multi-modal biometric system. Recently, @cite_12 have proposed an Impostor-based Uniqueness Measure (IUM) which is based on the location of mean impostor score relative to the maximum and minimum of the impostor score distribution. Using both genuine and impostor scores, @cite_11 investigated the existence of biometric menagerie in a broad range of biometric modalities like 2D and 3D faces, fingerprint, iris, speech, etc.
@cite_14 @cite_15 @cite_13 @cite_12 @cite_11
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The issue of recognizability of subjects in biometric identification is of particular interest to the designers of these systems. We have applied the concept of Doddingtons biometric menagerie to the area of facial recognition. We performed a series of tests for the presence of goats, lambs, and wolves on FRGC 2.0 color image data. The data for the subjects that appeared at the extreme end of these tests was then visually examined. Even a cursory comparison of images showed that for this set of data, some images fell into the defined menagerie categories. Our tests show the statistical existence of these animal classifications within the constraints of this set of FRGC 2.0 data using the baseline matching algorithm. Ultimately, these tests were limited by the image data set and matching algorithm used. For further confirmation of the existence of the menagerie, the analysis must be expanded to include different image sets and matching algorithms.. Abstract : Performance variability in speech and speaker recognition systems can be attributed to many factors. One major factor, which is often acknowledged but seldom analyzed, is inherent differences in the recognizability of different speakers. In speaker recognition systems such differences are characterized by the use of animal names for different types of speakers, including sheep, goats, lambs and wolves, depending on their behavior with respect to automatic recognition systems. In this paper we propose statistical tests for the existence of these animals and apply these tests to hunt for such animals using results from the 1998 NIST speaker recognition evaluation. Recent research in biometrics has suggested the existence of the “Biometric Menagerie” in which weak users contribute disproportionately to the error rate (FAR and FRR) of a biometric system. The aim of this work is to utilize this observation to design a multibiometric system where information is consolidated on a user-specific basis. To facilitate this, the users in a database are characterized into multiple categories and only users belonging to weak categories are required to provide additional biometric information. The contribution of this work lies in (a) the design of a selective fusion scheme where fusion is invoked only for a subset of users, and (b) evaluating the performance of such a scheme on two public datasets. Experiments on the multi-unit CASIA V3 iris database and multi-unit WVU fingerprint database indicate that selective fusion, as defined in this work, improves overall matching accuracy while potentially reducing overall computational time. This has positive implications in a large-scale system where the throughput can be substantially increased without compromising the verification accuracy of the system. We present a framework, called uniqueness-based nonmatch estimates (UNE), which demonstrates the ability to improve face recognition performance of any face matcher. The first aspect of the framework is a novel metric for measuring the uniqueness of a given individual, called the impostor-based uniqueness measure (IUM). The UNE the maps face match scores from any any face matcher into non-match probability estimates that are conditionally dependent on the probe image's IUM. Using this framework we demonstrate: (i) an improved generalization of matching thresholds (and, subsequently, improved matching accuracy), (ii) a score normalization technique that improves the interoperability for users of different face matchers, and (iii) the predictive ability of IUM towards face recognition accuracy. Studies are conducted on an operational dataset with 16,000 subjects using three different face matchers (two commercial, one proprietary) to demonstrate the effectiveness of the proposed framework. It is commonly accepted that users of a biometric system may have differing degrees of accuracy within the system. Some people may have trouble authenticating, while others may be particularly vulnerable to impersonation. Goats, wolves, and lambs are labels commonly applied to these problem users. These user types are defined in terms of verification performance when users are matched against themselves (goats) or when matched against others (lambs and wolves). The relationship between a user's genuine and impostor match results suggests four new user groups: worms, doves, chameleons, and phantoms. We establish formal definitions for these animals and a statistical test for their existence. A thorough investigation is conducted using a broad range of biometric modalities, including 2D and 3D faces, fingerprints, iris, speech, and keystroke dynamics. Patterns that emerge from the results expose novel, important, and encouraging insights into the nature of biometric match results. A new framework for the evaluation of biometric systems based on the biometric menagerie, as opposed to collective statistics, is proposed.