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Universidade Federal do Rio Grande do Norte

Centro de Ciências Exatas e da Terra

Departamento de Informática e Matemática Aplicada

Mestrado Acadêmico em Sistemas e Computação

A probabilistic analysis of the biometrics

menagerie existence: case study in

fingerprint data

Rayron Victor Medeiros de Araújo

Natal-RNFebruary 2016

Rayron Victor Medeiros de Araújo

A probabilistic analysis of the biometrics menagerie

existence: case study in fingerprint data

Master’s thesis presented to the GraduateProgram in Systems and Computing of theDepartment of Informatics and Applied Math-ematics at the Federal University of RioGrande do Norte as a partial requirementfor the degree of Master in Systems and Com-puting.

Universidade Federal do Rio Grande do Norte – UFRN

Departamento de Informática e Matemática Aplicada – DIMAp

Supervisor: Márjory Da Costa-Abreu

Natal-RNFebruary 2016

Araújo, Rayron Victor Medeiros de. A probabilistic analysis of the biometrics menagerieexistence: case study in fingerprint data / Rayron VictorMedeiros de Araújo. - Natal, 2016. 48f: il.

Orientadora: Profa. Dra. Marjory Cristiany da Costa Abreu.

Dissertação (Mestrado) - Universidade Federal do Rio Grandedo Norte. Centro de Ciências Exatas e da Terra. Programa de Pós-Graduação em Sistemas e Computação.

1. Biometric menagerie. 2. Zoológico biométrico. 3.Impressão digital. I. Abreu, Marjory Cristiany da Costa. II.Título.

Catalogação da Publicação na FonteUniversidade Federal do Rio Grande do Norte - UFRN

Sistema de Bibliotecas - SISBI

RAYRON VICTOR MEDEIROS DE ARAÚJO

A probabilistic analysis of the biometrics menagerie existence: a

case study in fingerprint data

Esta Dissertação foi julgada adequada para a obtenção do título de mestre em

Sistemas e Computação e aprovado em sua forma final pelo Programa de Pós-Gradua-

ção em Sistemas e Computação do Departamento de Informática e Matemática Aplicada

da Universidade Federal do Rio Grande do Norte.

__________________________________________________________

Dr. Bruno Motta de Carvalho – UFRN

(Presidente)

__________________________________________________________

Dr. Uirá Kulesza – UFRN

(Coordenador do Programa)

Banca Examinadora

____________________________________________________________

Dra. Marjory Cristiany da Costa Abreu – UFRN

(Orientadora)

____________________________________________________________

Dr. Daniel Sabino Amorim de Araujo – UFRN

(Examinador)

____________________________________________________________

Dr. George Darmiton da Cunha Cavalcanti – UFPE

(Examinador)

Fevereiro, 2016

I dedicate this work to my wife, Rosi Costa.

Acknowledgements

First and above all, I praise God, for providing me this opportunity and grantingme the capability to proceed successfully. I thank my parents and my wife for all thesupport during this period. I thank also to my supervisor Marjory, which, again, believeme to perform this work.

ResumoAté pouco tempo atrás o uso de biometria se restringia a ambientes de alta segurançae aplicações de identificação criminal por razões de natureza econômica e tecnológica.Contudo, nos últimos anos a autenticação biométrica começou a fazer parte do dia adia das pessoas. Desde então, alguns problemas de autenticação entraram em evidência,como a impossibilidade de votar numa eleição porque o indivíduo não tinha sua impressãodigital reconhecida. Isso acontece, pois os usuários de um sistema biométrico podem terdiferentes graus de acurácia, principalmente em sistemas de utilização em larga escala.Alguns desses usuários podem ter dificuldade na autenticação, enquanto outros podem ser,particularmente, vulneráveis à imitação. Estudos recentes investigaram e identificaramesses tipos de usuários, dando-lhes nomes de animais: Sheep, Goats, Lambs, Wolves,Doves, Chameleons, Worms e Phantoms. O objetivo desse trabalho é avaliar a existênciadesses tipos de usuários em uma base de dados de impressões digitais e propor uma novaforma de investigá-los, baseando-se no desempenho das verificações entre amostras. Nossosresultados identificaram a presença de goats, lambs, wolves, chameleons e phantoms, alémde demonstrar a ausência de worms e doves, em um sistema biométrico proposto.

Palavras-chaves: Biometric menagerie, Zoológico biométrico, Impressão digital.

AbstractUntil recently the use of biometrics was restricted to high-security environments andcriminal identification applications, for economic and technological reasons. However, inrecent years, biometric authentication has become part of daily lives of people. Sincethen, some authentication problems are in evidence, as the inability to vote in an electionbecause the individual did not have his fingerprint recognized. This is because users of abiometric system may have di�erent degrees of accuracy, especially in large-scale systems.Some people may have trouble authenticating, while others may be particularly vulnerableto imitation. Recent studies have investigated and identified these types of users, givingthem the names of animals: Sheep, Goats, Lambs, Wolves, Doves, Chameleons, Wormsand Phantoms. The aim of this study is to evaluate the existence of these users types ina database of fingerprints and propose a new way of investigating them, based on theperformance of verification between subjects samples. Our results identify the presence ofgoats, lambs, wolves, chameleons and phantoms, as well as demonstrate the absence ofworms and doves in a proposed biometric system.

Key-words: Biometric menagerie, Fingerprint.

List of Figures

Figure 1 – Registration and recognition (verification and identification) steps of abiometric system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Figure 2 – Ridge ending and bifurcation example. . . . . . . . . . . . . . . . . . . 16Figure 3 – NBIS’s MINDTCT module system architecture . . . . . . . . . . . . . 17Figure 4 – Minutiae comparison within the fingerprint . . . . . . . . . . . . . . . . 19Figure 5 – The relationship between genuine score and impostor score, and the

biometric menagerie. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Figure 6 – Examples of fingerprints samples from the CASIA-FingerprintV5 database 28Figure 7 – The distribution region where we find possible chameleons. . . . . . . . 31Figure 8 – Verification scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32Figure 9 – Distribution of the mean worst genuine match scores . . . . . . . . . . 34Figure 10 – Histogram of the mean worst genuine match scores . . . . . . . . . . . 35Figure 11 – Examples of Goats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36Figure 12 – Distribution of the mean higher impostor match scores . . . . . . . . . 36Figure 13 – Histogram of the mean higher lamb scores . . . . . . . . . . . . . . . . 37Figure 14 – Histogram of the mean higher wolf scores . . . . . . . . . . . . . . . . . 38Figure 15 – Examples of Lambs and Wolves . . . . . . . . . . . . . . . . . . . . . . 39Figure 16 – Relationship between genuine and impostor match scores . . . . . . . . 40Figure 17 – Examples of New Animals . . . . . . . . . . . . . . . . . . . . . . . . . 41Figure 18 – Number of fingers by user belonging to each animal. . . . . . . . . . . . 42Figure 19 – Location of the indicative of animals . . . . . . . . . . . . . . . . . . . 44Figure 20 – Number of samples per threshold . . . . . . . . . . . . . . . . . . . . . 45

List of Tables

Table 1 – Resume for the eight animals . . . . . . . . . . . . . . . . . . . . . . . . 22Table 2 – CASIA-FingerprintV5 fingers names . . . . . . . . . . . . . . . . . . . . 27Table 3 – Percentage of users in the goat score distribution. . . . . . . . . . . . . . 33Table 4 – Results and Probabilities for the Presence (or Absence) of New Animals 40Table 5 – Percentage distribution of users by fingers occurrence for each animal. . 43Table 6 – CASIAV5 with threshold value 20 . . . . . . . . . . . . . . . . . . . . . 43Table 7 – CASIAV5 with threshold value 30 . . . . . . . . . . . . . . . . . . . . . 43Table 8 – CASIAV5 with threshold value 40 . . . . . . . . . . . . . . . . . . . . . 43

List of abbreviations and acronyms

EER Equal Error Rate, ou Taxa de Erro Igual

FAR False Acceptance Rate, ou Taxa de Falsa Aceitação

FRR False Rejection Rate, ou Taxa de Falsa Rejeição

MINDTCT Minutiae Detector, ou Detector de Minúcias

PIN Personal Identification Number, ou Número de Identificação Pessoal

ROC Receiver Operating Characteristics, ou Característica de Operação doReceptor

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111.2 Goals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2 Biometrics Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.1 Verification and Identification . . . . . . . . . . . . . . . . . . . . . . . . . 142.2 Fingerprint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152.3 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162.4 Matching Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

2.4.1 BOZORTH3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182.4.2 Genuine Match Score e Impostor Match Score . . . . . . . . . . . . 202.4.3 False Acceptance and False Rejection . . . . . . . . . . . . . . . . . 20

3 The Biometric Menagerie . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.1 The First Four Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223.2 The Other Four Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

3.2.1 Chameleons, Phantoms, Doves and Worms . . . . . . . . . . . . . . 233.3 Evaluation Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 243.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4 Experiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

4.1 Experimental Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.2 Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274.3 Animals Existence Test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

4.3.1 First Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284.3.2 New Animals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

4.4 Animals Analysis Based on Samples . . . . . . . . . . . . . . . . . . . . . . 304.4.1 Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

5 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

5.1 Analysis of Animals Existence . . . . . . . . . . . . . . . . . . . . . . . . . 335.1.1 Goats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335.1.2 Lambs and Wolves . . . . . . . . . . . . . . . . . . . . . . . . . . . 345.1.3 Chameleons, Phantoms, Worms, and Doves . . . . . . . . . . . . . 37

5.2 Analysis by Fingers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415.3 Animals Analysis Based on Samples . . . . . . . . . . . . . . . . . . . . . . 42

6 Final Remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Bibliography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

11

1 Introduction

Biometrics is the science of recognizing humans based on the physical or behavioraltraits of an individual. Examples of these traits include face, fingerprint, iris, hand geometry,voice, and gait (JAIN; ROSS; PRABHAKAR, 2004; JAIN; FLYNN; ROSS, 2008). Theuse of biometrics as an authentication mechanism is already present in daily life. Severalapplications make use of biometrics, such as access to restricted environments, accessto devices and resources, money withdrawal, elections, countries immigration, grantingaccess to nuclear facilities, etc. This is due to the lower cost of deployment of such systems(RILEY; KLEIST, 2005) and from the inherent benefits of exchange for other forms ofidentification, such as the use of a personal identification number (PIN) and password.

Along with the increase in applications that make use of biometrics as a form ofauthentication, it was necessary to identify which users are having inconsistent performance(e.g. di�culties in identifying, impersonation, etc) (HECK et al., 1997). In (DODDINGTONet al., 1998), this inconsistency was shown and, from it, was introduced the concept ofbiometrics menagerie (also called biometrics zoo), which categorizes the users accordingto their associated performance identification. Four user groups were identified and eachgroup received the name of an animal that resembles in its behavior, namely: sheep, goats,lambs and wolves. More recently, in (YAGER; DUNSTONE, 2007), was identified morefour other user groups: doves, chameleons, worms and phantoms. Each of these animalsbehave di�erently under the system.

This new method of investigation and analysis of biometric systems di�ers fromtraditional evaluation methods that focus on global error statistics such as ROC (ReceiverOperating Characteristic) curves and EER (Equal Error Rate). These statistics are usefulfor evaluating a biometric system as a whole, but ignore problems associated with users.

1.1 MotivationSeveral studies demonstrate that only a small fraction of a biometric system users

presents inconsistencies in the identification performance, but users within the biometricsmenagerie compose much of this fraction (DODDINGTON et al., 1998; POH; KITTLER,2008; POH; KITTLER, 2009; HICKLIN; WATSON; ULERY, 2005). Thus, there are manyreasons which encourage the characterization of an individual in the biometrics menagerie:

• Allows to evaluate the biometric system performance, while showing the reasonswhy the performance is poor.

Chapter 1. Introduction 12

• Allows to identify which individuals are subject to impersonation and which areimpersonating other users (RATTANI; POH; ROSS, 2012).

• Allows to add protection mechanism according to the behavior of each individualsgroup, either by using a selective multimodal fusion strategy (POH et al., 2013;WANG et al., 2012; ROSS; RATTANI; TISTARELLI, 2009) or simply by analyzingthe reasons why the users have a poor identification performance and trying tomitigate the e�ects of such problems (WITTMAN; DAVIS; FLYNN, 2006).

1.2 GoalsIn this work we investigate the presence of users groups (biometrics menagerie)

in di�erent biometric identification systems based on fingerprint and analyse the reasonswhy they are present or not. Also, we investigate how each finger of a user behaves inthe biometric menagerie trying to find evidence of users who have more than one fingerbehaving like each biometric animal. To do so, the following goals were set:

• Demonstrate how to use the biometric menagerie to evaluate a real biometric system.

• Check the presence of users groups (biometric menagerie) in di�erent databases.

• Analyse how to mitigate the e�ects caused by the users in the biometric menagerie.

• Analyse how di�erent fingers of a user behave in a biometric system.

1.3 OverviewThis work is organized as follows: Chapter 2 present some basic concepts in biometric

systems, as well as an introduction to fingerprint and the algorithms used. In Chapter 3 aprecise definition of each biometric menagerie member is presented, followed by Chapter 4,where the experiments for this study are proposed. Chapter 5 present a case study infingerprint which investigates the reasons why a user is present in a biometric menageriemember. Finally, the paper concludes with a summary of the results and possible futurework.

13

2 Biometrics Systems

In biometrics authentication systems, there are often inconsistencies in the identifi-cation of some users, who may be falsely rejected or accepted in the system. That beingsaid, those responsible by such biometric systems are interested in identifying groups ofusers who have poor identification performance because they can be the cause of a dispro-portionate number of verification errors. An analysis of users and the traits in commonbetween them, can expose a key vulnerability in a biometric system, and addressing thisvulnerability, you can make more robust biometric systems (YAGER; DUNSTONE, 2009).

A biometric authentication system recognizes (or verify) the identity of an individual(or someone claiming to be that individual). This operation may be necessary for severalreasons, but the primary purpose, in many applications, is to prevent impostors to haveaccess to protected resources. Traditional methods use passwords and mechanisms fortokens (ID cards), however these forms of identity representation can be easily lost,shared or even stolen. The biometrics-based authentication provides a natural and reliablesolution for certain aspects of identity management, because you can use fully or partiallyautomated schemes to recognize individuals based on biological characteristics of them(JAIN; ROSS; PRABHAKAR, 2004). In some applications, biometrics may be used inconjunction with passwords and ID cards to enhance the security level.

Biometrics o�ers certain advantages such as negative recognition and non-repudiationthat cannot be provided by tokens and passwords. The negative recognition is the processby which the system determines that a particular individual is registered in the systemeven if that individual deny such access. This is especially critical in applications such aselections, in which an impostor might try to vote multiple times under di�erent names.The non-repudiation is a way to ensure that an individual who accesses a certain facilitycan not subsequently deny that access (e.g. a person accesses a computer and then saysthat an impostor must have used fake credentials).

Biometric systems use various physiological or behavioral characteristics includingvoice, fingerprint, face, iris, ear geometry, odor, finger/hand-geometry, hand veins, sig-nature, gait, keystroke or even DNA to establish the identity of the individual (BOLLE;PANKANTI, 1998)(WAYMAN et al., 2005). However, in this work, we use only fingerprintas biometrics and, therefore, from here on when we mention the term biometrics we arereferring to fingerprint.

Chapter 2. Biometrics Systems 14

2.1 Verification and IdentificationDepending on the application, a biometric system can work in verification or

identification mode, see Figure 1. In verification mode, the biometric fingerprint systemtries to validate the identity of a person comparing the fingerprint captured by the sensorwith its own template1 stored in the database system. In these systems, the individual whowants to be recognized enters some data indicating his identity, usually a card, usernameor PIN, and the system makes a one-to-one comparison to determine whether or not heis who it claims to be. Thus, it is as if we did the following question: “is this user whohe claims to be?”. In the criminal area, for example, when you have a suspect and afingerprint at the scene, we can make a verification to determine if the suspect is or notguilty. In this case there is no need for a PIN or card because we are trying to validate theindividual’s identity. Verification is generally used for positive recognition, in which theaim is to prevent multiple people using the same identity.

However, in an identification biometric system, the system recognizes an individualby making a comparison with all templates stored in the database. Therefore, the systemmakes a comparison one-to-many searching for the individual’s identity, and may failif the individual does not have registered in the system. Systems like this answer thequestion: “whose biometric is this”?. In the criminal area, when there is only a fingerprintat the crime scene, and no suspects, we can make an identification searching for thatfingerprint in the database of the government agency competent with the registry of people.The identification is a critical component in negative recognition applications in whichthe system establishes whether a person is who he/she denies being. The purpose of thenegative recognition is to prevent the same person using multiple identities.

From Figure 1 we can note that the feature extraction, matching and decisionmodules are of fundamental importance in a biometric system. This is because, dependingon the choice of the algorithm modules mentioned, the system performance may bea�ected in di�erent ways. In this work, specifically, the feature extraction and matchingmodules used are MINDTCT (WATSON et al., 2007b) (fingerprint minutiae detector) andBOZORTH3 (WATSON et al., 2007a), respectively. These modules are contained in theNBIS2 (NIST Biometric Image Software), developed by the Federal Bureau of Investigation(FBI).1

Set of extracted fingerprint features of an individual; usually position and orientation. During the

registration of an individual in the system this set of features is stored in a database and often called

template.

2<http://www.nist.gov/itl/iad/ig/nbis.cfm>

http://www.nist.gov/itl/iad/ig/nbis.cfm


Figure 1 – Registration and recognition (verification and identification) steps of a biometricsystem

Source: the author. The quality assessment module determines whether an image from thesensor can be e�ectively used by the feature extraction module.

2.2 FingerprintThe fingerprints have been used for over a century and are the most commonly used

form of biometric identification. The fingerprint identification is commonly used in forensicscience to support criminal investigations, and biometric systems, such as commercialauthentication devices.


The fingerprint of an individual is unique, although su�ers changes due to wear orcuts throughout life. A fingerprint is formed from a fingerprint ridges pattern. A ridgeis defined as a single curved segment, and the valley as the region between two adjacentridges. The minutiae, which are discontinuities in the local ridge flow pattern, provide thefeatures that are used for authentication (JAIN et al., 1997).

In (GALTON, 1982) was defined a set of features for fingerprint identification,which since then has been redefined to include additional types of features. However, mostof these features are not commonly used in fingerprint identification systems. Instead,the set of minutiae types is restricted to only two: bifurcations and ridge endings. Ridgeendings are the points where the curves of the ridge ends, and bifurcations are where theridges, from a single path, split into two paths. Figure 2 illustrates an example of a ridgeending and a bifurcation. In this example, the black pixels represent ridges and the whitepixels represent valleys of a fingerprint.

Figure 2 – Ridge ending and bifurcation example.

(a) Ridge ending (b) Bifurcation

It is very rare to have fingerprint images of perfect processing quality. Usually, theycan be degraded or corrupted with noise elements due to several factors including changesin skin and printing conditions.

2.3 Feature ExtractionIn order to identify the fingerprint minutiae which are used by the matching

algorithm, a feature extraction algorithm is needed. For this study we used the MINDTCTalgorithm (WATSON et al., 2007b). The reasons why the MINDTCT was used are: isopen source; it is widely used commercially, mainly by the FBI and is also widely used inother studies on biometrics.

The MINDTCT works as follows: it receives a fingerprint image as input and findsall minutiae in the image, giving each minutiae its location, orientation, type and quality.


The MINDTCT architecture is shown in Figure 3, and can be divided into the followingsteps: (i) generation of image quality map; (ii) binarization; (iii) minutiae detection; (iv)removing false minutiae; (v) counting of ridges between a minutia point and its nearestneighbor; and (vi) minutiae quality assessment.

Figure 3 – NBIS’s MINDTCT module system architecture

Due to variation of image quality in a captured fingerprint image, NBIS analyzesthe image and locates areas that are degraded. Several features are measured, includinglow-contrast regions, incoherent ridges flows and high curvatures. These three conditionsrepresent unstable areas in the image where the minutiae detection is unreliable and,together, they are used to represent the levels of quality in the image. An image qualitymap is generated by integrating these three features. The images are divided into non-overlapping blocks, where a quality level between one and five is assigned to each block.

The minutiae detection step scans the binarized fingerprint image, identifying localpixels patterns indicating a ridge ending or a bifurcation. A set of minutiae patterns isused to detect points of candidate minutiae. Subsequently, false minutiae are removed andthe remaining candidates are considered true minutiae in the image.

In the last step, a measure of confidence/quality is associated with each minutiaepoint detected. Even after the removal step, potential false minutiae remain in minutiae list.A robust quality measure can help get around this. Two factors are combined to producea quality measure for each minutiae point detected. The first factor is taken directly fromthe minutia point location within the quality map described above. The second factor isbased on a simple statistical of pixels intensity (mean and standard deviation) near theminutia point.


2.4 Matching AlgorithmA biometric system rarely finds two samples of biometric characteristics from an

user that look exactly alike. This is due to acquisition conditions (e.g. noise in fingerprintdue to sensor malfunction), changes in ambient light, variations in user interaction with thesensor (e.g. capture only part of the printing). In fact, a perfect match between two sets ofcharacteristics may indicate the possibility of playing an attack on system. The variabilityobserved in the set of biometric characteristics of a individual is called intra-class variation,and variability between sets of characteristics originated from two di�erent individuals iscalled inter-class variation.

To determine whether two fingerprints are from the same person, an algorithm isrequired to calculate how they are related. This algorithm is called a matching algorithm.In this study we used the BOZORTH3 matcher (WATSON et al., 2007a) as matchingalgorithm. The reasons why the BOZORTH3 was used are the same as the MINDTCT.The BOZORTH3 matcher is a minutiae-based algorithm that calculates a score (matchscore) that represents the similarity of two fingerprints. This algorithm is a modifiedversion of a matching algorithm between fingerprints written by Allan S. Bozorth whileworking in the FBI.

2.4.1 BOZORTH3The BOZORTH3 uses only the location (x, y) and orientation (t) of the minutiae

points to compare the prints, and it’s invariant to rotation and translation. The algorithmcan be described in three steps: (i) construction of two minutiae comparison tables intra-fingerprint; (ii) construction of inter-fingerprint compatibility table; and (iii) generation ofthe match score using digital inter-fingerprint compatibility table.

For each fingerprint being compared a minutiae comparison table intra-fingerprintis established. In this table are stored relative distances and orientations from the minutiaeof the same fingerprint. These are the measures that provide rotation and translationinvariance to the algorithm.

Figure 4 illustrates the measures between minutiae that are used. There are twominutiae in this example. The minutia k is at the bottom left of the fingerprint and isrepresented by a point in the location (xk, yk) and the arrow pointing down and right isthe orientation tk. A second minutia j is at the top and right with up and right orientation.To measure the relative translation, the distance dkj between the location of the minutiaeis calculated. This distance will remain relatively constant between corresponding points indi�erent fingerprints of the same person, even if there is rotation or displacement betweenprints.

Measuring the relative rotation is more complicated. The objective is to calculate, for


Figure 4 – Minutiae comparison within the fingerprint

Source: Watson et al. (2007a). Distances and orientations between two minutiae.

each minutia pairing, the angle between the orientation of the minutia and the intermediateline between the two minutiae. Thus, these angles remain relatively constant over theintermediate line between the minutiae, regardless of the amount of rotation that is appliedin print. In Figure 4, the intermediate line angle ◊kj between the minutiae is calculatedby taking the arctangent of the line inclination. The angles —k and —j are calculated inrelation to the intermediate line by adding ◊kj and each minutiae orientation t. Entriesin the minutiae comparison table are stored in increasing distance order and the table istrimmed in point whose distance exceeds a maximum threshold.

The next step is to look for compatibility between the two intra-fingerprint tables.The entries of the tables are compatible if: (i) the corresponding distances and (ii) therelative angles of the minutiae are within a specified tolerance, that by default is only1 unit. A inter-fingerprint compatibility table is generated and includes only matchingentries. An entry of this table incorporates two pairs of minutiae, a pair from trainingtemplate and a pair from test template. Thus, a inter-fingerprint table entry indicates thata minutiae pair from training template correspond to a minutiae pair from test template.

At the end of the second step the compatibility table consists of a list of compatibleassociations between two potentially minutiae pairs. Each association is a link in acompatibility graph. The matching algorithm then runs and connects table entries inclusters, combining compatible clusters and accumulating a matching score. The greater


the number of compatible associations, the greater the matching score will be, and themore likely of the two digital be from the same person.

2.4.2 Genuine Match Score e Impostor Match ScoreConsider a population of users P and a set of match scores S. For each pair of

users j, k œ P, there is a set S(j, k) µ S containing all verification results obtained bycomparing one of the j templates against a reference template belonging to k.

The genuine score of k-th user is represented by the set Gk = S(k, k), and theimpostor score by the set Ik = S(j, k) fi S(k, j) for all j ”= k. In other words, the genuinescore is the result of comparing two fingerprint samples of the same person, while theimpostor score is the result of comparing two fingerprints samples of di�erent people.

For each user k are assigned two values: one indicating how well it matches itself(gk) and another indicating how well it matches with other individuals (ik). Moreover, wecan think of a probability density function fS{•|j, k} as the distribution of match scoresobtained from comparing samples of j users against the user templates k.

2.4.3 False Acceptance and False RejectionIn a verification, when the match score exceeds a defined threshold value ÷ we say

that fingerprints are from same person. An impostor score that exceeds the threshold ÷

results in a false acceptance while a genuine score that is below the threshold ÷ results ina false rejection.

The false acceptance rate (FAR) of a biometric system can be defined as thefraction of impostor scores exceeding the threshold ÷. Similarly, the false rejection rate(FRR) of a system can be defined as the fraction of genuine scores that fall below thethreshold ÷. By adjusting the value of ÷ changes the values of FAR and FRR, but fora given biometric system, it is not possible to reduce both errors rates simultaneously(JAIN; FLYNN; ROSS, 2008).

21

3 The Biometric Menagerie

In a biometric identification system, it is often that the system recognition per-formance varies significantly from one user to another. The result is that some users arefalsely rejected by the system, while others are easily imitated by impostors. In the lastdecade, several groups of problematic users have been characterized, and each group wasgiven the name of an animal that, similarly, reflects its behavior. The concept of biometricmenagerie was formalized by Doddington et al. (1998) and the first animals were as follows:

a) Sheeps represent the majority of the population and are, usually, easy toidentify;

b) Goats are users generally di�cult to identify. These users tend to have a lowmatch score when compared with themselves. They represent a disproportionateincrease in the false rejection rate FRR;

c) Lambs are individuals easy to imitate. Other users tend to have a relatively highmatch score when compared to lamb users. They represent a disproportionateincrease in the false acceptance rate FAR;

d) Wolves are users good at imitating. When compared against other users, theytend to have a high match score. Like lambs, they represent a disproportionateincrease in the false acceptance rate FAR.

Only goats, lambs and wolves contribute to a negative impact on the error rate of thesystem, so users in those categories are called weak users. Many papers (DODDINGTON etal., 1998; POH; KITTLER, 2008; POH; KITTLER, 2009; HICKLIN; WATSON; ULERY,2005) confirm that the weak users constitute only a small fraction of the population ina biometric identification system; however their contribution in the error rate can bedisproportionately high.

In a posterior study, by Yager e Dunstone (2007), new animals were distinguished.Are they:

a) Worms are individuals who, like goats, tend to have a low match score whencompared against themselves. In addition, they also tend to receive a highmatch score when compared against some other individuals;

b) Chameleons are individuals who receive high match score when comparedagainst any user, either himself, or another user;

c) Phantoms are individuals who receive low match score when compared againstany user, including themselves;

Chapter 3. The Biometric Menagerie 22

d) Doves represent the most perfect users of the biometric system. They, likesheep, receive high match score when compared against themselves, but alsoreceive low match score when compared against other users.

Table 1 – Resume for the eight animals

First four animals Four other animals

Sheep easy to identify Chameleons easy to identify and good at imitating

Goats di�cult to identify Phantoms di�cult to identify and poor at imitating

Lambs easy to imitate Doves easy to identify and poor at imitating

Wolves good at imitating Worms di�cult to identify and good at imitating

The Table 1 shows a resume for the behavior of the eight animals. It is importantto note that animals do not necessarily represent a distinct and mutually exclusive subsetof users (DODDINGTON et al., 1998; WAYMAN, 2004). Indeed, it is possible that theydo not even exist in a real system. The animals may be better understood as a tendencybehavior and thus an individual may be more susceptible to attack than another, forexample.

The study conducted on (DODDINGTON et al., 1998) was based on speechrecognition data, but the concept of animals may be applied to any area in biometricsidentification. Subsequently, several other studies have shown the animals existence in otherbiometrics. Wayman (2004) demonstrated the existence of lambs and wolves in data basedon fingerprints with a high degree of significance; Wittman, Davis e Flynn (2006) examinedthe existence of animals in face recognition; Poh e Kittler (2008) shown, individually, thephenomenon in di�erent biometrics; among others, as (YAGER; DUNSTONE, 2007).

All aforementioned studies presented methods to deal with the existence of animalsin a biometric system.

With regard to the relationship between user groups, some questions arise. Forexample, the fact that a user is, notoriously, lamb makes it more likely to be goat?Doddington et al. (1998), in their study, reported a positive correlation between lambsand wolves users. In fact, lambs and wolves reflect a symmetry of matching algorithms.Wittman, Davis e Flynn (2006) showed that goats on average are also very poor whencompared against other users. In other words, goats are very poor wolves.

3.1 The First Four AnimalsThe sheeps represent the majority of the population of a biometric system. On

average, they tend to match well against themselves but not necessarily poor against otherusers.

Goats are users who are often di�cult to identify, and are characterized by havingconsistently low match score when compared with themselves. They tend to a�ect adversely


the system performance to account for a disproportionate portion of false rejections.

Lambs are users easy to imitate. In general, they tend to produce a high matchscore when compared by other users. This is because its set of biometric characteristicssignificantly overlaps with the other users in the database. Similarly, wolves are good atimitating other users, and tend to receive a higher match score when compared againstother users. As we can see, the definitions of lambs and wolves are symmetrical and bothresult in a disproportionate number of false acceptances of the biometric system.

Just like goats, there is not necessarily a distinct population of lambs and wolves.Instead, users show a varying degree of behavior lamb and wolf, respectively.

In (UNE; OTSUKA; IMAI, 2008), a measure known as wolf attack probability,which measures the maximum probability of success to impersonate a victim has beenproposed.

3.2 The Other Four AnimalsGoats, lambs and wolves were defined in terms of genuine or impostor match scores.

The other four new animals, by contrast, are defined in terms of the relationship betweenthe genuine score and impostor score. As well as the first four animals, the four otheranimals exist for at a certain degrees and do not represent a distinct and mutually exclusivesubset. However, for convenience, we will set a threshold to indicate the presence of thenew animals, as done in (YAGER; DUNSTONE, 2007).

The Figure 5 shows how the animals are defined in terms of the distribution ofgenuine scores and impostor scores. Marks Q

1

and Q3

represent 25% of the values in thedistribution.

Let G the set of the genuine score average of all users: G = fikœPgk. Rank all usersk œ P by increasing value of gk. Let GH µ P be the set of users in the 25% higher genuinescore range in G. Let GL µ P be the 25% of users with the lowest genuine score. Similarly,let I = fikœPik, and IH µ P be the 25% of users with the highest impostor score, andIL µ P the 25% of users with the lowest impostor score (YAGER; DUNSTONE, 2010).

3.2.1 Chameleons, Phantoms, Doves and WormsIntuitively, chameleons are users who always seem to be similar to others. They

have both high Gk and Ik, and belong to the set GH fl IH . Therefore, they tend to receivehigh match score on all verifications, either against themselves or against another user.Chameleons rarely cause false rejections, but are likely to cause false acceptances. Userswho have a very generic biometric characteristic, and that weighs heavily on matchingalgorithms may be chameleons.


Figure 5 – The relationship between genuine score and impostor score, and the biometricmenagerie.

Source: Yager e Dunstone (2010)

Phantoms belong to the set GL fl IL. They are exactly the opposite of chameleons,then they tend to receive lower match score in any verification.

Doves are the best users of the system and belong to the set GH fl IL. They receivehigh match score when compared with themselves and low match score when comparedagainst other users. Typically, dove users have a quite unique biometric characteristicswhen compared to other users in the database.

Worms are the worst users of a biometric system. They belong to the set GL fl IH .When they exist, they are responsible for a disproportionate number of system errors.

3.3 Evaluation MethodsIn the biometric menagerie, users tend to belong to one or more groups of animals.

This happens because some users are performing better than others. Thus, the animals


definitions, in essence, simply state that the distributions of match scores depend on theuser. Thus, the presence of animals in a biometric system is established without having toexplicitly label users.

In order to show that the distributions of match scores are user dependent, we canperform the hypothesis test. The null hypothesis in (DODDINGTON et al., 1998), for thefirst four animals, is that there are no significant di�erences between the performances ofthe users, that is, given a fS{•|k, k}, for example, it does not depend on k. The relevantdistributions and their respective null hypotheses are:

a) Goats - the density of interest is fS{•|k, k} and the null hypothesis is that thedensity does not depend on k.

b) Lambs - the density of interest is fS{•|j, k} and the null hypothesis is that thedensity does not depend on k for every j ”= k.

c) Wolves - the density of interest is fS{•|j, k} and the null hypothesis is thatthe density does not depend on j for every j ”= k.

Using F-Test and Kruskal-Wallis test, the author showed that the null hypothesiswas rejected with – = 0, 01 significance. However, due to the normality assumption, the F-Test is not intended for biometric data (YAGER; DUNSTONE, 2010). The Kruskal-Wallistest is a nonparametric method to test whether a set of samples come from the samedistribution, i.e. to test for di�erences between independent distributions. Therefore, wecan determine whether the di�erent samples observed indeed suggest di�erences betweenthe distributions or are only casual variations that may be expected from random samplesfrom the same population. Moreover, Kruskal-Wallis test is similar to the simple analysis ofvariance (ANOVA), except that the scores are replaced by rank (DANIEL, 1989). Becauseit is nonparametric, the method of Kruskal-Wallis is best suited for biometrics and we useit to test the null hypothesis of the first four animals.

The Equation 3.1 can be used to calculate the Kruskal-Wallis test. In the equation,N is the total number of samples, g is the size of the user population, n is the number ofsamples per user and ri is the score sum of user samples of positions i in the N orderedvalues.

H = 12N(N + 1) ◊

gÿ

i=1

r2

i

n≠ 3(N + 1) (3.1)

The null hypothesis is rejected if H Ø ‰2

–:g≠1

. We can find ‰2

–:g≠1

entering into aChi-squared table distribution the values g ≠ 1 degrees of freedom and – significance level.

The new animals are defined in terms of the relationship between genuine andimpostor match score. These relationships are based on ranks and quartiles, so it isexpected that the number of users in each animal group to be p ◊ |P|, where p = (1/4)2


(as GH , GL, IH and IL represent 25% users each, the intersection of two of such sets is 25%of 25%, or (1/4)2). This is under the assumption that users in GH or GL and IH or IL areindependent.

The null hypothesis in Yager e Dunstone (2007), for the four new animals, is thatstatistics of genuine and impostor scores of a user are independent, so there is about 1/16of the population of users in each animal group. Then a population of chameleons, forexample, will be indicated by a high number of users with high genuine and impostorscore combined (i.e. |GH fl IH | ∫ 1/16 ◊ |P|).

In order to demonstrate the existence of new animals, we can define that we areinterested in the set of chameleons C (the analysis is the same for other animals). Wherec is the number of chameleons, c = |C|. The null hypothesis is that the probability of aparticular individual to be chameleon is p = 1/16. Since each individual is independent,it is a binomial experiment with n = |P| attempts. The hypothesis is bilateral and notdirectional. Also, suppose that the number of chameleons observed is greater than theexpected number. In order to test the null hypothesis, we calculated the probability of morethan c chameleons. This probability can be calculated using the binomial distribution:

f(c; n, p) =nÿ

i=c

An

i

B

pi(1 ≠ p)n≠i (3.2)

For large values of n, the binomial distribution can be approximated using a normaldistribution with the expected value np(1 ≠ p). Suppose the desired confidence level is –,then the null hypothesis is rejected if f(c; n, p) < – (YAGER; DUNSTONE, 2010).

For being bilateral, the null hypothesis allows a symmetrical argument if the numberof users is less than expected. Thus, the null hypothesis is rejected in two ways: if there isa significantly low or high number of chameleons.

3.4 ConclusionThe menagerie biometric animals are basically represented by their genuine and

impostor match scores. From these two pieces of information we can perform statisticaltests to demonstrate their existence or absence of a biometric system.

27

4 Experiments

This chapter will present the experimental works done. The chapter is organizedas follow: Section 4.1 the experimental data is presented, while Section 4.2 describes howthe experimental data was preprocessed. Section 4.3 presents, in detail, the experimentalwork done for the animals existence test, while Section 4.4 presents the experimental workfor the animals analysis based on samples.

4.1 Experimental DataIn this work we have used the database CASIA-Fingerprint V5 containing 20,000

fingerprint images of 500 users (CASIA-FINGERPRINTV5, 2013). Each user contributedwith 40 fingerprints of the eight fingers (excluding the little finger) with five prints for eachfinger. The eight fingers were named as the Table 2. Users in this database were instructedto rotate their fingers with various pressure levels in order to obtain a significant intraclassvariation.

Table 2 – CASIA-FingerprintV5 fingers names

Right hand fingers Left hand fingersR0 thumb L0 thumbR1 index L1 indexR2 middle L2 middleR3 ring L3 ring

Where it was necessary to divide the five samples of each finger we have dividedas follows: 3 for training and 2 for testing. Figure 6 shows two examples of fingerprintsamples from di�erent users in CASIA-FingerprintV5 base.

4.2 Pre-ProcessingWe have extracted the fingerprints characteristics using the MINDTCT, which

takes as input a fingerprint image and produces as output a template of the set of minutiae,characterized by its location and orientation. We have used the BOZORTH3 matcher tocalculate the similarity between fingerprints from the templates. As the match score ofBOZORTH3 is the similarity between fingerprints, the higher this value, the more likelythat the two fingerprints are the same person.

Chapter 4. Experiments 28

Figure 6 – Examples of fingerprints samples from the CASIA-FingerprintV5 database

Both prints have noise from the acquisition sensor. The left fingerprint is rotated about90o, while the right fingerprint is quite degraded.

4.3 Animals Existence Test

4.3.1 First AnimalsIn order to demonstrate the existence of the first four animals in CASIA Fin-

gerprintV5 database, as already mentioned previously, we have used the Kruskal-Wallismethod to test the null hypothesis with a traditional significance level of 0.05. In orderto identify weak users who belong to these groups of animals, if they exist, we use thestatistical framework based on the concept of percentiles of match scores, as proposed byDODDINGTON et al.. We get the p-th percentile of the N ordered values by calculatingthe rank as follows:

r = p

100 ◊ N + 12 (4.1)

The percentile value for each animal group depends on the database and the applicationnature. However, the values used to finding goats, lambs and wolves were the same asspecified in (DODDINGTON et al., 1998).

In this experiment, the templates were not divided into training templates and testsince one of the limitations of Kruskal-Wallis test is that it requires at least 5 samples foreach distribution and the CASIA-FingerprintV5 database has only 5 fingerprint samplesfor each user finger.


From the user’s fingerprints templates, we calculate, for each finger, the performancein each animal group.

a) Goats: We calculate the goats statistics as follows:

– for each user’s finger we compare each sample with all other of the samefinger, storing the worst result.

– thus, as each user’s finger has 5 samples, are 5 results. It is from the distribu-tion of these results that we test the null hypothesis.

– we calculate the average of these 5 results to take the goat score (i.e. genuinescore).

– lastly, we have a goat score for each finger of the 500 users.– users whose finger goat score is below the 2.5 percentile are considered goats

for that finger.

b) Lambs: To show the existence and identify the lambs the following tests weremade:

– for each user’s finger, we compare all the samples of each other users with itsamples and stores the best (highest) result. Note that each finger sample intest is attacked by all other samples from other users (between the fingers ofthe same type), so we have 5 results. We use these results to test the nullhypothesis.

– we calculate the average of these 5 results to take the lamb score.– lastly, we have a lamb score (or impostor score) for each finger of the 500

users.– users whose finger lamb score is above the 97.5 percentile are considered

lambs for that finger.

c) Wolves: The test for the wolves is symmetrical to the test for lambs, requiringonly reverse the samples comparison:

– for each user’s finger, we compare each sample to all the samples of each otherusers (same finger type), storing the best result. We test the null hypothesisfrom these results.

– we average these results and obtain the wolf score.– lastly, we have a wolf score (or impostor score) for each finger of the 500

users.– users whose finger wolf score is above the 97.5 percentile are considered wolves

for that finger.


4.3.2 New AnimalsAs the definition of new animals is based on the relationship between genuine

and impostor score, the existence test of the new animals comes from the values alreadycalculated in the existence test for the first animals. Therefore, in this test there is also nodivision of templates for training and testing. The null hypothesis for the new animals isthat the genuine score and the impostor score of each user is independent and thereforethere is approximately 1/16 of the user population in each animal. That said, we use theconcepts of rank and quartiles to calculate the amount of possible animals in each group,and the null hypothesis was tested using equation 3.2.

As an example, we can suppose we are interested in calculating the amount ofchameleons. Chameleons are individuals who have both high genuine score and impostorscore (GH fl IH). Figure 7 shows the distribution region where we find the possiblechameleons. The quartiles are calculated as follows: we ordered the genuine and impostormatch score performances of users and calculate the 25th (mark Q

1

) and 75th (mark Q3

)percentile of each performance. The chameleons are users in that both genuine score andimpostor score are above the 75th percentile (mark Q

3

).

Since the number of possible chameleons was calculated, we defined a confidencelevel – to the presence of the animal in the database. In this work, the confidence levelwas set at – = 0.05. The null hypothesis is rejected if f(c; n, p) < –, which, in this case, c

is the amount of possible chameleons, n the size of the user population and p the initialprobability that a particular individual be a chameleon.

If, for example, the null hypothesis is rejected for chameleons – thus demonstratingthe existence of the animal in the database with 0.05 of significance – users in that regionwill be considered chameleons.

4.4 Animals Analysis Based on SamplesThe definition of animals proposed by DODDINGTON et al. and YAGER; DUN-

STONE is exclusively based on the average of the user’s match scores. However, an analysisof the results of the comparisons made between user’s samples can exhibit behaviors notshown by the approach of the above authors. Moreover, biometric systems are quitesensitive to algorithm parameters. Factors such as sensor, environment, feature extractionalgorithm, matching algorithm, etc., all can be adjusted to get a better system performance.

In this experiment, we have analyzed how the presence of animals varies accordingto the value of the verification threshold. We also verify, based on samples, the behaviorof each animal with a fixed threshold value.


Figure 7 – The distribution region where we find possible chameleons.

4.4.1 ExperimentFor this experiment we use only the right thumb finger (R0). From the test

templates of each user, we do a verification with all training templates using threeverification threshold values: 20, 30 and 40 (score ranging from 0 to Œ). Thus, since wehave 2 templates of test and 3 templates of training for each finger, and we have 500users, we do 2 ◊ 3 ◊ 500 ◊ 500 = 1.500.000 verifications for each threshold value. Figure 8summarizes how the comparisons were made.

In order to reduce the number of results, only the comparisons with a value equal toor greater than the threshold were analyzed. Thus, a sample that does not have computedresults simply means that it is considered sheep to this threshold value. Despite beingrecommended for the BOZORTH3 matcher the value threshold of 40 for commercial use(WATSON et al., 2007a), we have used, for convenience, the fixed threshold value set at20 to analyze the behavior of each animal based on a comparison between samples.


Figure 8 – Verification scheme.

33

5 Results and Discussion

In this chapter we presents the results for the animals existence analysis experimentand the samples based analysis experiment.

5.1 Analysis of Animals ExistenceBased on match scores distribution we analysed the presence of the animals in each

one of the eight fingers for all users. The null hypothesis for goats, lambs and wolves isthat the performance of match is approximately similar for all users, while for chameleons,phantoms, worms and doves is that the genuine and impostor scores does not depend onthe user.

5.1.1 GoatsThe Figure 9 shows the distribution of the mean worst genuine match scores with

a confidence interval of 97.5%. As a high genuine score represents a better matching, anyuser with a goat statistic below the red line can be reasonably considered a goat, comparedwith the remaining users. However, the presence of goats is not clear. If there were no userdependency, as proposed by the null hypothesis, only one user out of forty would be belowthe red line.

The histogram of the distribution of the mean worst genuine match scores is shownin Figure 10. In black, the values corresponding to the users below percentile 2.5. It isapparent that the average of the worst genuine scores concentrates on lower values. Infact, about 40% of users have the average genuine match score below 20. While 27.7%users have the genuine match score above 40, recommended value for commercial use ofBOZORTH3. Table 3 summarizes the percentage of users per verification threshold valueand, through it, we can note that the L0 finger have both a lower number of users below20 and a higher number of users above 40.

Table 3 – Percentage of users in the goat score distribution.

Right hand fingers Left hand fingersR0 R1 R2 R3 L0 L1 L2 L3

users below 20 44% 42% 36% 43% 30% 36% 36% 45%users above 40 24% 26% 29% 23% 39% 29% 31% 21%

The Kruskal-Wallis test was applied to the eight fingers of all 500 users of the CASIA-FingerprintV5 database and the null hypothesis was rejected with 0.05 of significance

Chapter 5. Results and Discussion 34

Figure 9 – Distribution of the mean worst genuine match scores

for all fingers. Therefore, the existence of goats in all eight fingers, at least for the useddatabase, can be a�rmed.

Figure 11 shows 3 examples of samples from each of the two individuals withthe worst genuine scores (goat score). We can see that there is large intraclass variationbetween samples of each user; the samples, among them, are of di�erent printing regions,and all have a lot of noise from what appears to be impressions marks of people who haveused the acquisition sensor earlier.

5.1.2 Lambs and WolvesThe Figure 12 shows the distribution of impostor scores to lambs (left) and wolves

(right) at a confidence level of 97.5%. As a lower impostor score represents a worse matching,users above the red line are considered, respectively, lambs and wolves in relation to otherusers. The histogram of the distribution of lamb scores and wolf scores can be seen inFigure 13 and Figure 14, respectively.

Again, the Kruskal-Wallis test was applied to the database users and the nullhypothesis was rejected with 0.05 of significance for all fingers, thereby demonstrating theexistence of lambs and wolves.


Figure 10 – Histogram of the mean worst genuine match scores

We note that from the 13 users with the highest lamb scores, 12 are among the 13highest wolf scores. This may be a reflection of the existing symmetry in the BOZORTH3matching algorithm, meaning that the value of match score does not vary much when wechange the direction of comparison of fingerprints. From the 13 users with the worst wolfscores, 2 users are also among the worst goat scores, showing that the relationship betweenwolves and goats, reported by Wittman, Davis e Flynn (2006), although less significant,also exists in this system.

The Figure 15 shows examples of samples from the two users with highest tendencyto lamb and wolf. These two users attack each other and between them the average matchscore is 25, and the second sample from user 1, compared to the first sample from user 2


Figure 11 – Examples of Goats

(a) User 1

(b) User 2

Above: (a) three samples of the user with higher goat tendency. Below: (b) the user withthe second higher goat tendency.

Figure 12 – Distribution of the mean higher impostor match scores

receives the maximum match score between them, which is 29. Due to the nature of thematching algorithm, which takes into account the distance and direction between pairs


Figure 13 – Histogram of the mean higher lamb scores

of minutiae, it is di�cult to find from the images the common features between samples.However, the reasons why the samples are attacking may be several, from ghost imagescaused by latent fingerprints on the sensor, common features or even because the templatesof the images registered in system are from part of the fingerprint.

5.1.3 Chameleons, Phantoms, Worms, and DovesThe Figure 16 shows the relationship between the genuine match score and impostor

match score. The Table 4 summarizes the experimental results and the correspondingprobability values for the new animals.


Figure 14 – Histogram of the mean higher wolf scores

From results presented in Table 4 we can notice a significant presence, or absence,of each of new animals. The interpretation of such systems (containing or not each animal)is that, in some systems, the probability of a user being falsely rejected is not independentof the probability of being falsely accepted. These values show that there is a probability< 0.01% of the existence (or absence) of each of new animals are the result of chance.

Due to the significant absence of worms and doves, the Figure 16 illustrates apositive correlation between the means of genuine and impostor match scores for all fingers.The result is a significant population of phantoms in the lower left corner. Further analysisshows that most of the phantoms are people whose fingerprints are heavily damaged. Thiskind of fingerprint increases the di�culty of extracting features, leading to unreliable


Figure 15 – Examples of Lambs and Wolves

(a) User 1

(b) User 2

Above: (a) three user samples with higher tendency to lamb and wolf. Below: (b) samplesof the user with the second higher tendency.

biometric templates and that results in a low match score in almost all comparisons.

The Figure 17 contains examples of fingerprints of each of new animals. Figure 17shows two examples of fingerprint samples from the user that have the highest wormtendency. Initially we note that the samples are from di�erent regions of fingerprint andare at di�erent angles. The fact that the fingerprint is rotated does not interfere in thematch score, since the BOZORTH3 matching algorithm is invariant to rotation, and thisanalysis is the same for samples from other animals. However, when samples of a user arefrom di�erent regions of the same fingerprint, it typically impacts on a low genuine score,but not necessarily result in a high impostor score. Therefore, the presence of worms in abiometric system may indicate deficiencies in the matching algorithm. Figure 17b displaystwo samples of the most phantom user. As mentioned above, most of the phantoms inthis system are justified by their fingerprints damaged; It is exactly what happens in thesamples of Figure 17b.


Table 4 – Results and Probabilities for the Presence (or Absence) of New Animals

Right hand fingers Left hand fingersR0 L0

worms doves chameleons phantoms worms doves chameleons phantoms

test absent absent present present absent absent present presentprobability <0.01% <0.01% <0.01% <0.01% <0.01% <0.01% <0.01% <0.01%

number of users 6 12 64 65 5 6 70 72R1 L1









number of users 5 6 62 67 10 6 62 52In the animals absent case was applied a symmetrical argument to the existence test.

Figure 16 – Relationship between genuine and impostor match scores

The Figure 17c contains samples of a chameleon user. An analysis of the imagesof the users that belong to this group indicates that most samples have large areas ofcapture, ridges with clearly well-defined structures and consistent capture regions. Thisexplains the high genuine score average. What is not clear is the cause of the high scoreimpostor. The reasons for the high impostor score of chameleons can be several, such as:fingerprints with features based on quite generic minutiae; persistent noise between sensor


captures that are not eliminated, but are considered by the feature extraction algorithm;fraud in the fingerprint registration system; duplicate registrations, etc.

Finally, the Figure 17d displays samples from the user with the highest tendencyto be a dove. The doves in this system have a high genuine score for the same reasonsthat the chameleons, i.e., good quality, well-defined structures and a large capture area.However, the analysis of the images of the users of this group, we can notice unusualfeatures (e.g. well-defined fingerprint core with several minutiae; one or two deltas, etc.)in relation to other individuals of the base. This leads to both high genuine scores and lowimpostor scores.

Figure 17 – Examples of New Animals

(a) Worms (b) Phantoms

(c) Chameleons (d) Doves

Above: (a) two samples from the user with the highest worm tendency; (b) two samplesfrom the user with the highest phantom tendency. Below, (c) two samples from the userwith the highest chameleon tendency; and (d) two samples from the user with the highestdove tendency.

5.2 Analysis by FingersIt is well accepted that the presence of goats, lambs and wolves can harm the user

authentication. However, if a user is a goat in one of his fingers, it will also be for theothers fingers? In order to figure out how is the finger by finger performance behavior, weidentified what the animal to each of the fingers of all users.


In this experiment, we consider only users with average scores within the percentile2.5. Therefore, we consider only those users who have a higher animal’s behavior trend. Thedistribution of the fingers considered to belong to the group of users who are more likelyfor each animal is shown in Figure 18, while Table 5 shows the percentage of occurrencesper finger.

Initially, we note that the number of fingers belonging to the wolves and lambs arethe same. This, once again, reflects the symmetry matching algorithm, even though thereare 16% of users belong to only one set of these two animals.

The number of users who have only a finger belonging to one of the animals isrepresented by the majority. In this case, the mere use of a biometric authentication systemutilizing more than one finger, can solve this specific problem.

Analyzing the goats samples, we note that approximately 10% of users have threeor more fingers regarded as a goat, and only one user has five fingers considered goat.

Figure 18 – Number of fingers by user belonging to each animal.

5.3 Animals Analysis Based on SamplesThe Table 6 shows the results, obtained from samples analysis experiment, for the

threshold value 20, while Table 7 and Table 8 show the results for the threshold values 30


Table 5 – Percentage distribution of users by fingers occurrence for each animal.

1 finger 2 fgrs 3 fgrs 4 fgrs 5 fgrs

Goat 69.01% 21.13% 5.63% 2.82% 1.41%Lamb 77.38% 21.43% 1.19% 0% 0%Wolf 77.38% 21.43% 1.19% 0% 0%

and 40, respectively. The rows of each table are divided by the number of training sampleswhich exceed the threshold value when compared with the same user test samples. Thus,the first column of the first row is the amount of test samples that, when compared withthe same user training samples, did not exceed the threshold value in any comparison.The first column of the second row represents the quantity of test samples exceeding thethreshold value in a comparison, and so on. The following columns show the number ofattacks per number of test samples exceeding the threshold value. Therefore, the columnvalue “4 atck.” and first line, of Table 6, means that, for 5 times, a test sample that didnot exceed the threshold value when compared with training samples of the same user,su�ered four attacks from other samples of other users.

Table 6 – CASIAV5 with threshold value 20.

hits 1 attack 2 atcks 3 atcks 4 atcks +5 atcks total

0 hits 103 10 2 1 5 8 +77

by 1 hit 100 8 9 7 2 32 +215

user 2 hits 189 21 16 11 10 68 +466

3 hits 608 56 34 38 22 356 +2.096

total 1000 95 122 171 156 +2.310



0 hits 178 5 1 x x x 7

by 1 hit 152 12 2 x 1 x 20

user 2 hits 220 23 6 4 x 2 +57

3 hits 450 52 15 15 9 3 +178

total 1000 92 48 57 40 +25



0 hits 272 x x x x x 0

by 1 hit 183 1 x x x x 1

user 2 hits 214 x x x x x 0

3 hits 331 2 x x x x 2

total 1000 3 0 0 0 0

The Tables 6, 7 and 8, as well as Figure 5, have regions representing the presenceof animals in the system. However, in this case, the analysis is made on the samples.


A significant number of samples with 0 hits may indicate the presence of goats in thesystem or at least demonstrate that a considerable portion of the samples registered is notgood enough. Similarly, a significant number of samples with 3 hits is an indicative of thepresence of sheeps in the system.

Figure 19 – Location of the indicative of animals

The Figure 19 shows which are an indicative of the presence of each animal inthe tables. According to the figure, and with the results shown in Tables 6, 7 and 8, wenotice that increasing the verification threshold value we decrease the amount of attacksand consequently, the false acceptance rate FAR. On the other hand, we also increase thenumber of verifications which have not reached the threshold value, which leads to anincrease in false rejection rate FRR.

Figure 20 shows the behavior described in the preceding paragraph. In this figure,only the new animals are analyzed. The doves are represented by the test samples thathad three hits and no attack; chameleons are the samples that had three hits and at leastone attack; the phantoms that neither had hit and no attacks; and worms by the samplesthat had no hits and were attacked.

With the verification threshold value at 20, more than half of the test sampleshad three hits (doves and chameleons), and only 103 samples obtained no hits (phantomsand worms). However, from those had more than 3 hits, 504 were also attacked. Thismay represent a disproportionately high value on false acceptance rate. Increasing thethreshold to 40, the amount of samples in the animals that may represent a false acceptancedecreases (chameleons and worms), while the amount of phantoms that may represent afalse rejection increases. This behavior shows that we can not reduce the false acceptancerate and false rejection rate simultaneously by simply changing the threshold.


Figure 20 – Number of samples per threshold

X axis: threshold used in the verification. Y axis: number of samples which tend to animal.

46

6 Final Remarks

A paper from this work was accepted into the XV Brazilian Symposium on Infor-mation Security and Computer System (SBSeg 2015) with the same title of this work.

In this study, we investigated the presence of the biometric menagerie in a fingerprintauthentication biometric system. We showed the users fingers with the higher tendencyto an animal and the reasons why this occurs, as well as how many fingers each user hasin each animal group. We have analyzed how the presence of animals varies according tothe value of the verification threshold. Also, we verified, based on fingerprint samples, thebehavior of each animal with a fixed threshold value.

With a statistically significant evidence, we demonstrated the presence of goats,lambs and wolves in all eight fingers. The results showed that the presence of a highnumber of goats users may be because of a large intraclass variation between samples ofeach user. While for lambs and wolves, was not clear the reasons for a high number ofusers in theses animals. We observed a high symmetry between lambs and wolves, andindicated that this may be due to the matching algorithm.

The four new animals (worms, doves, chameleons and phantoms) also were identifiedwith a statistically significant evidence. We showed that chameleons and phantoms arepresent, while worms and doves are significantly absent in all eight fingers. The presenceor absence of the four new animals reflects the properties of the matching algorithm, thepopulation of users, or a combination of both.

The reasons why a particular animal group exists are varied and complex. Theydepend on a number of factors, including the registration process, feature extraction andmatching algorithm, quality of the fingerprint captured by the sensor and the intrinsicproperties of the user population.

In our analysis by fingers, we showed that only a small number of users that belongto an animal have more than one finger belonging to that animal. Moreover, from thoseindividuals who belongs to an animal, approximately 10% of users have three or morefingers regarded as a goat.

The present study brings to those responsible for a real biometric system a view ofthe practical way of how to use the biometric menagerie to evaluate it. For future work, weintend to use a more large database to analyse how the match score distribution behavesin large scale biometric system. Also, we intend to use the biometric menagerie to comparehow a real biometric system behaves before and after utilize cancelable biometrics.

47

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