Liveness Detection in Fingerprint Recognition Systems

Liveness Detection in Fingerprint

Recognition Systems

Examensarbete utf¨ort i Informationsteori vid Link¨opings tekniska h¨ogskola

av

Marie Sandstr¨om Reg nr: LITH-ISY-EX-3557-2004

Liveness Detection in Fingerprint

Recognition Systems

Examensarbete utf¨ort i Informationsteori vid Link¨opings tekniska h¨ogskola

av

Marie Sandstr¨om Reg nr: LITH-ISY-EX-3557-2004

Supervisor: Fredrik Claesson Examiner: Viiveke F˚ak Link¨oping 10th June 2004.

Avdelning, Institution Division, Department

Institutionen för systemteknik

581 83 LINKÖPING

X Examensarbete ISRN LITH-ISY-EX-3557-2004

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D-uppsats Serietitel och serienummer _{Title of series, numbering} ISSN

Övrig rapport

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URL för elektronisk version

http://www.ep.liu.se/exjobb/isy/2004/3557/

Titel

Title

Detektering av Artificiella Fingeravtryck vid Användarautenticiering Liveness Detection in Fingerprint Recognition Systems

Författare

Author

Marie Sandström

Sammanfattning

Abstract

Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods available today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today. Two approaches have been used to find out how good fingerprint recognition systems are in distinguishing between live fingers and artificial clones. The first approach is a literature study, while the second consists of experiments.

A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pressure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.

The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems were circumvented with all subjects' artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.

Nyckelord

Keyword

biometrics, identification, verification, fingerprints, fingerprint scanners, sensor attacks, artificial fingerprints, liveness detection

Abstract

Biometrics deals with identifying individuals with help of their biological data. Fingerprint scanning is the most common method of the biometric methods avail-able today. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint recognition systems are evolving and this study will discuss the situation of today.

Two approaches have been used to find out how good fingerprint recognition sys-tems are in distinguishing between live fingers and artificial clones. The first ap-proach is a literature study, while the second consists of experiments.

A literature study of liveness detection in fingerprint recognition systems has been performed. A description of different liveness detection methods is presented and discussed. Methods requiring extra hardware use temperature, pulse, blood pres-sure, electric resistance, etc., and methods using already existent information in the system use skin deformation, pores, perspiration, etc.

The experiments focus on making artificial fingerprints in gelatin from a latent fingerprint. Nine different systems were tested at the CeBIT trade fair in Germany and all were deceived. Three other different systems were put up against more extensive tests with three different subjects. All systems were circumvented with all subjects’ artificial fingerprints, but with varying results. The results are analyzed and discussed, partly with help of the A/R value defined in this report.

Acknowledgment

A number of people have helped me making this thesis work possible. First of all, I would like to thank everybody at ISY, especially my examiner Viiveke F˚ak, my supervisor Fredrik Claesson, the Data Transmission group for lending me their camera, and S¨oren Hansson, who helped me with the PCB production part of the experiments. I would also like to take the opportunity to thank the Finger-print Group at the National Laboratory of Forensic Science (SKL), especially Lena Hallberg and G¨oran Kidfelt.

I could not have performed the experiments without the participants. Thank you for lending me your fingerprints! The experiments at CeBIT would not have been able to perform without the companies who let me try their products. Thank you all for being patient with me!

I would also like to thank the following people who helped me in various ways: Ulf S¨oderholm, Fredrik Larsson, Bj¨orn Mellstr¨om, Bo Thun´er, Susanne Edlund, Maria Magnusson Seger, Johan Blomm´e, and Andreas Bergner.

Last but not least, I would like to thank Hannes Lindblom, whom I could not have done this thesis without.

Be aware. . .

Before you start reading this report, take a close look at your fingertips. Your papillary lines might form a loop, a whorl, or maybe it looks more like an arch. If you look even closer, you might be able to see some lines that split into two, a delta pattern somewhere, and maybe you can even see some sweat drops coming out of the pores on your fingertips.

Your fingerprint patterns are most certainly unique in the whole world. In theory, it is thus possible to identify you with help of a single fingerprint. If it was possible to make a copy of your fingerprint, your identity could then be used. Do you remember every single thing you touched today? Maybe you touched a few door handles, a glass, or a cup. Are you sure nobody has been watching you to be able to steal your fingerprint? Remember that a password can be changed, a new credit card can be bought, but a finger is not as easily changed.

Contents

1.6 Method criticism and limitations . . . 2

1.7 Target group . . . 3

1.8 Reading guide . . . 3

1.9 Notes . . . 4

2 Biometric overview 7 2.1 Identification and verification . . . 7

2.1.1 Methods of identification and verification . . . 8

2.1.2 Results from identification and verification procedures . . . . 9

2.2 Biometric techniques . . . 10 2.2.1 Physical characteristics . . . 10 2.2.2 Behavioral characteristics . . . 10 3 Fingerprints 13 3.1 History . . . 13 3.2 Today . . . 14 3.3 Fingerprint characteristics . . . 15

3.3.1 Classification and pattern types . . . 16

3.3.2 Terminology . . . 16 3.4 Enhancement techniques . . . 19 3.4.1 Processing techniques . . . 19 4 Fingerprint scanners 21 4.1 Fingerprint images . . . 22 4.2 Scanning techniques . . . 22 4.2.1 Optical sensors . . . 22 4.2.2 Solid-state sensors . . . 25 vii

viii Contents

4.2.3 Ultrasonic sensors . . . 27

4.3 Touch versus sweep . . . 28

4.4 Algorithms in fingerprint scanners . . . 29

4.4.1 Image enhancement . . . 30

4.4.2 Feature extraction and comparison . . . 30

4.5 Sensor attacks and protection schemes . . . 31

4.5.1 Registered finger . . . 31

4.5.2 Unregistered finger . . . 32

4.5.3 A twin’s fingerprint or a genetic clone . . . 32

4.5.4 Artificial fingerprint . . . 32

4.5.5 Others . . . 32

5 Liveness detection 35 5.1 Liveness detection in biometric systems . . . 35

5.2 Using extra hardware . . . 37

5.2.1 Temperature . . . 37 5.2.2 Optical properties . . . 37 5.2.3 Pulse . . . 37 5.2.4 Pulse oximetry . . . 38 5.2.5 Blood pressure . . . 38 5.2.6 Electric resistance . . . 38

5.2.7 Relative dielectric permittivity . . . 39

5.2.8 Combining ECG, pulse oximetry, and temperature . . . 39

5.2.9 Detection under epidermis . . . 40

5.2.10 Other claims . . . 40

5.3 Using existing information . . . 41

5.3.1 Skin deformation . . . 41

5.3.2 Pores . . . 41

5.3.3 Unique characteristic for each individual . . . 41

5.3.4 Perspiration . . . 42

5.4 Testing of liveness detection methods . . . 45

5.5 Relevance of liveness detection . . . 45

5.6 Other methods to limit spoofing . . . 45

5.6.1 Multiple snapshots of the same finger . . . 45

5.6.2 Multiple fingers . . . 46

5.6.3 Challenge-response . . . 46

5.6.4 Supervision . . . 46

5.6.5 Multi-modal biometrics . . . 47

5.6.6 Multiple identification/verification methods . . . 47

5.7 Additional comments . . . 47

6 History of artificial fingerprints 49 6.1 Albert Wehde’s work . . . 49

6.2 Six biometric devices point the finger at security . . . 50

Contents ix

6.4 Impact of Artificial ”Gummy” Fingers on Fingerprint Systems . . . 51

6.5 Body Check – Biometric Access Protection Devices and their Pro-grams Put to the Test . . . 52

6.6 An Investigation Into the Vulnerability of the Siemens ID Mouse Professional Version 4 . . . 52

6.7 Spoofing and Anti-Spoofing Measures . . . 53

6.8 Fooling Fingerprint Scanners – Biometric Vulnerabilities of the Pre-cise Biometrics 100 SC Scanner . . . 54

6.9 Evaluation of biometric security systems against artificial fingers . . 54

7 Experiment description 55 7.1 Making of the artificial fingerprint . . . 55

7.1.1 Enhancing the fingerprint . . . 55

7.1.2 Photographing the fingerprint . . . 58

7.1.3 Image processing . . . 58

7.1.4 Printing the image . . . 59

7.1.5 PCB production . . . 59

7.1.6 Gelatin solution . . . 59

7.2 Experiments at CeBIT . . . 62

7.3 Extensive experiments . . . 63

7.3.1 Subjects and input . . . 63

7.3.2 Software and hardware . . . 64

7.3.3 Experiment procedure . . . 65 8 Results 67 8.1 CeBIT . . . 67 8.2 Extensive experiments . . . 68 8.2.1 Results in numbers . . . 68 8.2.2 Results in percent . . . 71

9 Discussion and analysis 75 9.1 Experiment method . . . 75

9.1.1 Enhancing the fingerprint . . . 75

9.1.2 Photographing the fingerprint . . . 78

9.1.3 Image processing . . . 78

9.1.4 Printing the image . . . 80

9.1.5 PCB production . . . 80 9.1.6 Gelatin solution . . . 81 9.2 Experiments at CeBIT . . . 82 9.2.1 Sweeping sensors . . . 82 9.3 Extensive experiments . . . 83 9.3.1 Experience . . . 83 9.3.2 Subjects . . . 84

9.3.3 Initial test results . . . 84

x Contents

9.3.5 Comparison with results from previous studies . . . 88

9.4 Additional comments about artificial fingerprints . . . 89

9.4.1 Finding a quality latent fingerprint . . . 89

9.4.2 Alternative acquisition of fingerprint image . . . 89

9.4.3 Economies of scale . . . 89

9.4.4 Forging fingerprints . . . 90

9.4.5 Cooperation using latent print . . . 90

9.4.6 Using the artificial fingerprint . . . 91

10 Conclusion 93 10.1 Final conclusion . . . 93 10.2 Future work . . . 94 10.2.1 Liveness detection . . . 94 10.2.2 Artificial fingerprints . . . 94 10.2.3 Fingerprint scanners . . . 95 10.2.4 Alternative biometrics . . . 95 Bibliography 97 A Dictionary 103 B Material 109 B.1 Enhancing the fingerprint . . . 109

B.2 Photographing the fingerprint . . . 109

B.3 Image processing . . . 110 B.4 Printing . . . 110 B.5 PCB production . . . 110 B.6 Gelatin solution . . . 112 C Experiment details 113 C.1 Photographing . . . 113 C.2 Image processing . . . 114

C.3 Fingerprint images before and after image processing . . . 116

C.4 PCB production . . . 117

D Scanners used in extensive experiments 119 E Software used in extensive experiments 121 F Test results 123 F.1 Results per fingerprint scanner . . . 123

F.1.1 Identix . . . 123

F.1.2 Targus DEFCONTM_{Authenticator}TM _{. . . 124}

F.1.3 PreciseTM_{Biometrics 100 MC . . . 125}

F.2 Results per subject . . . 126

Contents xi

xii Contents

List of Figures

2.1 Enrollment, verification, and identification. [41] . . . 8

2.2 The relationship between FRR, FAR, and EER. [34] . . . 9

3.1 The three major pattern types: arches, loops, and whorls. [22] . . . 16

3.2 Core and delta points. [7] . . . 17

3.3 Minutiae details. [7] . . . 17

3.4 Sweat pores. [41] . . . 18

3.5 Cross-section of a papillary line. [48] . . . 18

4.1 An FTIR-based fingerprint sensor. [41] . . . 23

4.2 A fingerprint sensor using FTIR with a sheet prism. [41] . . . 24

4.3 Fingerprint sensing using optical fibers. [41] . . . 24

4.4 Electro-optical fingerprint sensor. [41] . . . 25

4.5 Capacitive fingerprint sensor. [41] . . . 26

4.6 An ultrasonic fingerprint sensor. [41] . . . 27

4.7 A sweeping sensor. [41] . . . 29

4.8 Typical structure of a fingerprint recognition system. [3, 42] . . . 29

5.1 West Virginia perspiration detection method. [9, 34] . . . 43

7.1 An overview of the process of making the mold. . . 56

7.2 Soot powder mixture and squirrel hair brush. . . 57

7.3 A mold with a gelatin solution on top of it. . . 61

7.4 A fingertip with a wafer-thin gelatin fingerprint on top of it. . . 62

8.1 The number of successful logins with real fingerprints. . . 69

8.2 The number of false acceptances with artificial fingerprints. . . 70

8.3 The success rate with real fingerprints. . . 71

8.4 The FAR with artificial fingerprints. . . 72

8.5 Mean values, in percent, for real and artificial fingerprints. . . 74

9.1 Results of unofficial tests with subject S2. . . 85

B.1 Materials used during production of PCB. [14] . . . 111

B.2 Gelatin used for making artificial fingerprints. [3] . . . 112

C.1 S1’s fingerprint before and after image processing. . . 116

C.2 S2’s fingerprint before and after image processing. . . 117

C.3 S3’s fingerprint before and after image processing. . . 117

D.1 Identix fingerprint scanner. [30] . . . 119

D.2 Targus DEFCONTM_{Authenticator}TM_{. [54] . . . 120}

D.3 PreciseTM_{Biometrics 100 MC. [2] . . . 120}

Contents xiii

E.2 Screenshot of Softex Omnipass. [3] . . . 122 E.3 Screenshot of Precise BioManagerTM_{included in Precise Logon}

xiv Contents

List of Tables

6.1 Characteristics of a live finger compared to a gelatin artificial

fin-gerprint. [42] . . . 51

6.2 Experiment types. [42] . . . 52

7.1 Possible experiment types. [42] . . . 63

7.2 Testing order for the scanners in round one and two. . . 66

8.1 Results from attacks with artificial fingerprints at CeBIT. . . 68

9.1 The A/R value for all subjects, round one. . . 86

9.2 The A/R value for all subjects, round two. . . 86

F.1 Results of the Identix fingerprint scanner for real fingerprints. . . 123

F.2 Results of the Identix fingerprint scanner for artificial fingerprints, round one. . . 124

F.3 Results of the Identix fingerprint scanner for artificial fingerprints, round two. . . 124

F.4 Results of the Targus fingerprint scanner for real fingerprints. . . 124

F.5 Results of the Targus fingerprint scanner for artificial fingerprints, round one. . . 125

F.6 Results of the Targus fingerprint scanner for artificial fingerprints, round two. . . 125

F.7 Results of the Precise fingerprint scanner for real fingerprints. . . 125

F.8 Results of the Precise fingerprint scanner for artificial fingerprints, round one. . . 126

F.9 Results of the Precise fingerprint scanner for artificial fingerprints, round two. . . 126

F.10 Sum of values per user for real fingerprints. . . 126

F.11 Success rate, FRR, and FAR, per subject for real fingerprints. . . 127

F.12 Sum of values per subject for artificial fingerprints, round one. . . . 127

F.13 Sum of values per subject for artificial fingerprints, round two. . . . 127

F.14 Values in percent, per subject for artificial fingerprints, round one. . 128

Chapter 1

Introduction

This chapter contains a short introduction to the thesis. The goal, purpose, method, and target group will be presented, method criticism and limitations will be discussed, and a reading guide will give the reader a quick guide to each chapter.

1.1

Background

The use of biometric systems are growing every day. Fingerprint scanning is the one biometric identification method available today that is mostly used. The security of fingerprint scanners has however been questioned and previous studies have shown that fingerprint scanners can be fooled with artificial fingerprints, i.e. copies of real fingerprints. The fingerprint systems are evolving and this study will discuss the situation of today.

1.2

Basic terminology

The term artificial fingerprint will be used in this report to refer to artificially created fingerprints, as compared tofake fingers/fingerprints which may also in-clude modifications of live fingers. Many other writings in the area use the terms

artificial finger or artificial fingertip, but these terms have not been used in this report to emphasize that the artificial fingerprints made from latent fingerprints, are in fact thin small prints and not entire artificially created fingers or fingertips. The termslive finger/fingerprint andreal finger/fingerprint will be used to denote a finger/fingerprint which is part of a living body.

2 Introduction

1.3

Goal

Two different approaches to the fingerprint scanner area will be covered in this report. The theoretical approach will discuss liveness detection, i.e. the fingerprint scanners’ ability to distinguish between live fingers and artificial clones. Different liveness detection methods will be presented and analyzed with regards to attacks with artificial fingerprints.

The empirical approach consists of examining the fingerprint scanners’ ability to withstand an attack of an artificial fingerprint using techniques based on earlier research made in [42] and [57]. More information about the method is found in section 1.5.

1.4

Purpose

Several reports [3, 42, 57] have noted successful attacks on fingerprint systems using artificial fingerprints. Since the fingerprint scanner market is growing and the technology is evolving, new products that can withstand attacks with artificial fingerprints might have seen the light today. This report will give a further exami-nation of the fingerprint scanner area to clarify whether or not fingerprint systems can be trusted or if they are too insecure to be used today.

1.5

Method

For the theoretical approach, a literature study has been performed. Articles, proceedings, books, etc. have been read, discussed and analyzed.

Prior to this report, a number of experiments have been performed based on earlier research made in [42, 57]. A latent fingerprint on a piece of glass was the starting-point for the creation of the artificial fingerprint. This starting-starting-point was chosen to simulate the user’s lack of awareness that the fingerprint was being stolen from him/her, as if the latent print was taken from a drinking-glass. The method used is described in more detail in chapter 7 on page 55.

1.6

Method criticism and limitations

Glasslike surfaces is only one of the possible surfaces a fingerprint can be found on. To investigate all possible surfaces, would require an enormous effort and a lot of time, and is therefore not part of this thesis.

1.7 Target group 3

Using an artificial fingerprint is only one of the possible attacks on a fingerprint system. Attacks at the sensor level will be described shortly in section 4.5 on page 31, but describing all the other possible attacks is outside the scope of this report.

1.7

Target group

This report has a number of different target groups: • Manufacturers of fingerprint recognition systems.

• Companies considering starting to use a fingerprint recognition system. • Users of fingerprint recognition systems.

• Researchers who want to continue researching the field of fingerprint recogni-tion systems, especially when it comes to liveness detecrecogni-tion and attacks with artificial fingerprints.

• Students in the field of computer science, information technology, etc., who have an interest in the security field and especially biometrics.

Since this report has so many different target groups, different parts of the report are relevant to different groups of people. The reader is not presumed to have previous knowledge about computer security, biometrics, or fingerprint recognition systems. The reading guide in section 1.8, is recommended for the reader who quickly wants to find the relevant parts for his/her specific purpose.

1.8

Reading guide

This section contains a short description of each chapter and appendix in the report. • Chapter 1 on page 1 contains a short introduction to the thesis. The goal, purpose, method, and target group are presented, and method criticism and limitations are discussed.

• Chapter 2 on page 7 gives the reader an introduction to the biometric area, describes important terms, and is a good starting point for the following chapters.

• Chapter 3 on page 13 presents the historical and present use of fingerprints, physical characteristics of fingerprints, and different enhancement techniques of fingerprints.

• Chapter 4 on page 21 discusses the use of fingerprint scanners, different scan-ning technologies, and briefly explains the algorithms used in the scanners.

4 Introduction

The chapter also describes possible ways of intrusion at the sensor level of fingerprint scanning systems, as well as the available protection schemes. • Chapter 5 on page 35 presents and discusses ideas about liveness detection,

i.e. fingerprint scanners’ ability to distinguish between live fingers and arti-ficial clones.

• Chapter 6 on page 49 summarizes the most important previous work in the field of artificial fingerprints.

• Chapter 7 on page 55 describes the method used in the experiments, the creation process of artificial fingerprints, and the material and software used in the experiments.

• Chapter 8 on page 67 presents the results from the experiments described in the previous chapter.

• Chapter 9 on page 75 analysis and discusses the method used in the experi-ments and the results acquired.

• Chapter 10 on page 93 contains a final conclusion and ideas about future work in the fields of liveness detection, artificial fingerprints, and fingerprint scanners.

• Appendix A on page 103 contains an alphabetized explanatory list of abbre-viations, technical terms, and medical terms used in this report.

• Appendix B on page 109 contains detailed information about the material used in the experiments.

• Appendix C on page 113 describes some parts of the experiment method in more detail.

• Appendix D on page 119 contains detailed information about the scanners used in the extensive experiments.

• Appendix E on page 121 contains detailed information about the software used in the extensive experiments.

• Appendix F on page 123 presents the detailed data (in numbers) of the results from the extensive experiments.

1.9

Notes

An extensive list with explanations of all important technical and medical terms and abbreviations, can be found in appendix A on page 103. The most important terms used will still be explained in the appropriate sections.

If nothing else is stated, references placed before a period in a sentence, refers to the sentence only, while a reference placed after the period refers to the whole

1.9 Notes 5

paragraph. A reference placed right before the colon before the beginning of a list, refers to the list after the colon. A reference which is placed after the last period in a figure subtitle, refers to the picture included and the whole subtitle.

Note that the results from the experiments performed prior to this report, only describe how good the systems are at protecting against attacks with gelatin arti-ficial fingerprints and not against any other attacks. The systems tested, do have other good and bad qualities that must be considered when purchasing a system. The experiments were performed to check the security of the systems with regards to attacks with artificial fingerprints, and not with regards to any other attacks or qualities of the systems.

Chapter 2

Biometric overview

Biometrics (also known as biometry) is defined as “the identification of an individ-ual based on biological traits, such as fingerprints, iris patterns, and facial features” [43].

2.1

Identification and verification

Identification and verification (also known as authentication) are both used to declare the identity of a user. Since the two terms identification and verification are easily mixed up, definitions are given below [41]:

• Identification: In an identification system, an individual is recognized by comparing with an entire database of templates to find a match. The system conducts one-to-many comparisons to establish the identity of the individual. The individual to be identified does not have to claim an identity (Who am I?). [41]

• Verification (authentication): In a verification system, the individual to be identified has to claim his/her identity (Am I whom I claim to be?) and this template is then compared to the individual’s biometric characteristics. The system conducts one-to-one comparisons to establish the identity of the individual. [41]

Before a system is able to verify/identify the specific biometrics of a person, the system requires something to compare it with. Therefore, a profile or template containing the biometric properties is stored in the system. Recording the charac-teristics of a person is calledenrollment. [57]

The processes of enrollment, verification, and identification are depicted graphically in figure 2.1 on page 8.

8 Biometric overview

Figure 2.1. Enrollment, verification, and identification. [41]

2.1.1

Methods of identification and verification

As a user, you can be identified or verified on the basis of: • Something you know: e.g. a password or a PIN.

• Something you hold: e.g. a credit card, a key, or a passport. • Something you are (biometrics): e.g. a fingerprint or iris patterns.

Using something you know and hold are two easy identification/verification solu-tions widely used today. Using something you know only requires a good memory, but can on the other hand easily be overheard, seen, or even guessed. An item you hold can be stolen and later on used or copied. Using biometrics might at first seem to overcome these problems, since fingerprints, iris patterns, etc. are part of

2.1 Identification and verification 9

your body and thus not easily misplaced, stolen, forged, or shared. This report might however give you some new insight about this subject.

One way to increase security in an identification/verification system is to combine two or more different identification/verification methods.

2.1.2

Results from identification and verification procedures

When results from identification or verification procedures are discussed, the fol-lowing terms will be used in this report:

• Success rate: The rate at which successful verifications or identifications are made compared to the total number of trials. [3]

• False rejection rate (FRR): The rate at which the system falsely rejects a registered user compared to the total number of trials. [3]

• False acceptance rate (FAR): The rate at which the system falsely accepts a nonregistered (or another registered) user as a registered one compared to the total number of trials. The FAR is in this report used in the identification version, as a contrast to verification procedures, where it measures if a user is accepted under a false claimed identity. [3]

• Equal error rate (EER): The common value of the FAR and FRR when the FAR equals the FRR. This is the value where both the FAR and FRR are kept as low as possible at the same time (see figure 2.2). A low EER value indicates a high accuracy of the system. [47]

Figure 2.2. The relationship between FRR, FAR, and EER. A big FRR often means a low FAR, and a big FAR often means a low FRR. The small EER value indicates that the security of the system is better. [34]

10 Biometric overview

2.2

Biometric techniques

Currently, there are many different techniques available to identify/verify a person based on biometrics [57]. These techniques can be divided into physical character-istics and behavioral charactercharacter-istics. All techniques have in common that acquired data is compared with templates enrolled earlier.

2.2.1

Physical characteristics

The following are examples of biometric techniques based on physical characteristics [3]:

• Fingerprint recognition: Fingerprint recognition systems scan the fingerprint pattern for recognition.

• Recognition of hand or finger: Recognition of hand or finger systems scan the entire hand or larger parts of the finger and makes a comparison of patterns in the skin (similar to fingerprint recognition systems). The difference between a fingerprint recognition system and a hand/finger recognition system, lie mostly in the size of the scanner and the resolution of the scanning array. • Face recognition: Face recognition systems detect patterns, shapes, and

shad-ows in the face.

• Face geometry: Face geometry systems work similar to face recognition sys-tems, but focus more on shapes and forms instead of patterns.

• Vein pattern recognition: Vein pattern recognition systems detect veins in the surface of the hand. These patterns are considered to be as unique as fingerprints, but have the advantage of not being as easily copied or stolen as fingerprints are.

• Retina recognition: Retina recognition systems scan the surface of the retina and compare nerve patterns, blood vessels and such features.

• Iris recognition: Iris recognition systems scan the surface of the iris to com-pare patterns.

2.2.2

Behavioral characteristics

The following are examples of biometric techniques based on behavioral character-istics [3]:

• Voice recognition: Voice recognition systems use characteristics of the voice, such as pitch, tone, and frequency.

2.2 Biometric techniques 11

• Signature recognition: Signature recognition systems measure pressure of the pen and frequency of writing to identify a person via a signature.

• Keystrokes dynamics: Keystrokes dynamics systems use statistics, e.g. time between keystrokes, word choices, word combinations, general speed of typing etc.

The authors of the book Handbook of Fingerprint Recognition suggest that all biometric identifiers are a combination of distinctive physiological and behavioral characteristics. For example, fingerprints may be physiological in nature but the usage of the input device (e.g. how a user presents a finger to the fingerprint scanner) depends on the person’s behavior. [41]

Chapter 3

Fingerprints

Already at the age of seven months, a foetus’ fingerprints are fully developed. The characteristics of the fingerprint does not change throughout the lifetime except for injury, disease, or decomposition after death. However, after a small injury on the fingertip, the pattern will grow back as the fingertip heals. [41, 49]

This chapter will begin with some important historical events concerning finger-prints, and specifically fingerprints as an identification tool. Then, a short glimpse will be taken at how society today looks at fingerprints. Fingerprint characteris-tics and enhancement techniques will also be discussed to give the reader a better platform to stand on, before reading the following chapters.

3.1

History

It is not justifiable to say that one single person was first to discover fingerprint patterns. Every human being has had papillary lines in front of her eyes for a very long time. It has only been a question of looking down at one’s own hands. However, there still exist some important historical events connected to fingerprints, which will be described shortly here.

• Already in ancient times, fingerprints appeared on pottery and cave paintings in Asia, Europe, and North America to denote authorship or identity [7]. • Fingerprints were not described in writing until the 17th century. In 1686,

Marcello Malpighi, a professor of anatomy at the University of Bologna, de-scribed papillary ridges in his treatise. [7, 44]

• In 1823, the Czech physician Jan Evangelista Purkynˇe, classified fingerprint patterns into nine basic types. Purkynˇe’s classification system, laid the foun-dation for future fingerprint identification systems. [7]

14 Fingerprints

• It was not until the later part of the 19th century that fingerprints found its use in personal identification through the two colonials in British India; Sir William Herschel and Dr. Henry Faulds. Dr. Faulds also devised a method of classification. [7, 44]

• Sir Francis Galton, a British anthropologist and a cousin of Charles Darwin, scientifically proved in the late 19th century that fingerprints do not change over the course of an individual’s lifetime, and that no two fingerprints are exactly alike. According to his calculations, the odds of two individual finger-prints being the same, are 1 in 64 billion. Galton identified the characteristics (minutiae) by which fingerprints can be identified, and these characteristics are therefore sometimes referred to as Galton’s details today. [7, 44]

Galton classified fingerprints as one of the three patterns, ”arches”, ”loops”, and ”whorls”. He found out that approximately 60 percent of all fingerprints are loops, around 30 percent whorls, and the remaining 10 percent are arches. Because of this uneven distribution, Galton then further subdivided the loops into ”inner” and ”outer” loops depending on whether the loop opened up toward the little finger or the thumb. Galton also was the founder of the classical fingerprint cards used in forensics. [7]

• In 1901, fingerprints were introduced for criminal identification in England and Wales. Galton’s observations, and revisions of those by Sir Edward Richard Henry, were used. This was the foundation of the Henry Classifica-tion System. [44]

• In 1918, Edmond Locard wrote that if 12 points (Galton’s deatils) were the same between two fingerprints, it would suffice as a positive identification. This is often referred to as the ”12 point rule”. Different countries have differ-ent rules though for iddiffer-entification, including own standards with a minimum number of points. [44]

• With the introduction of computers in the 20th century, the storing of fin-gerprint cards became computerized. [44]

• Sweden has since the 1st of April 2003 abandoned the 12 point rule. Today, a nonnumerical standard is used with no required minimum number of points for positive identification. [12]

3.2

Today

Fingerprint usage can be divided into three different areas [3]: • Security, as identification of individuals.

3.3 Fingerprint characteristics 15

• Personal characteristics and dermatoglyphics, often involved with horoscopes and similar nonscientifically proven prophesies.

The two first are by far the greatest areas. Fingerprint-based systems, used for security reasons, are so popular today that they have almost become the synonym for biometric systems [41]. Fingerprint-based systems will be further discussed in chapter 4 on page 21.

Enormous amounts of information is stored in a fingerprint database. For example, the total number of fingerprint cards (each card contains one impression each of the 10 fingers of a person) in the FBI fingerprint database has now exceeded 200 million, and is growing continuously. Most law enforcement agencies in the world use an AFIS (Automatic Fingerprint Identification System) today. These systems have increased the productivity and greatly reduced the cost of hiring and training human fingerprint experts. [41]

Since the discovery of the DNA structure in 1953, DNA has become more and more important in the society as a whole, as well as in forensics. With the science of cloning though, it can be questioned whether or not DNA can actually be used for identification purposes. If individuals can be cloned, DNA typing is as much help as it is in distinguishing identical twins. By definition, identical twins cannot be distinguished by DNA. The same problem does not occur with fingerprints. Even though the fingerprints of identical twins are very similar, automatic fingerprint system can successfully distinguish identical twins though with a slightly lower accuracy than nontwins. It should however be noted that the algorithms in some fingerprint systems may not be robust enough to detect these differences. [7, 35, 41, 51]

3.3

Fingerprint characteristics

You have probably looked at your own fingerprint at some point in your life and noticed the papillary lines on it. In fingerprint literature, the terms ridges and

valleys are used to describe the higher and lower parts of the papillary lines. The reason we have ridges and valleys on our fingers, is the frictional ability of the skin [48].

The formation of the ridges and valleys is a combination of genetic and environ-mental factors. The DNA gives directions in the formation of the skin of the foetus, but the exact formation of the fingerprint is a consequence of random events. The exact position of the foetus in the womb at a particular moment, and the exact composition and density of surrounding amniotic fluid, decide how every individual ridge will form. [25]

This is also the reason why the fingerprints on different fingers on the same individ-ual are different, and why identical twins have different fingerprints, see section 3.2 on page 14.

16 Fingerprints

3.3.1

Classification and pattern types

Fingerprints can be and have been classified in different ways throughout history, see section 3.1 on page 13. The Henry Classification System was the basis of modern day AFIS classification methods up until the 1990s. In recent years, the Henry Classification System has in most forensic departments been replaced by ridge flow classification approaches. These new classification methods use the distance between core and delta points, minutiae locations, and pattern type (the latter using the Henry Classification System). [22]

Fingerprints can be divided into the three major pattern types arches, loops, and whorls, depicted in figure 3.1. Loops are the most common fingerprint pattern [27]. These major pattern types can appear in different variations. For example, you can find plain or tented (narrow) arches, right or left loops, and spiral or concentric circles as whorls. Also, the different pattern types can be combined to form a fingerprint, e.g. a double loop, or an arch with a loop [5].

Figure 3.1. The three major pattern types: arches, loops, and whorls. These major pattern types can be divided further into different subgroups: right or left loops, plain or tented (narrow) arches, and spiral or concentric circles as whorls. There are also combinations of these different pattern types. [22]

3.3.2

Terminology

To understand the basics of fingerprints, the same approach as [41] uses, will be presented here. A fingerprint can be looked at from different levels; the global level, the local level, and the very-fine level [41].

At the global level, you find thesingularity points, calledcoreanddelta points, see figure 3.2 on page 17. These singularity points are very important for fingerprint classification, but they are not sufficient for accurate matching [41].

3.3 Fingerprint characteristics 17

Figure 3.2. Core and delta points marked on sketches of the two fingerprint patterns loop and whorl. Loops have one delta, whorls have two. Minutiae details are not shown. The number of intervening ridges from delta to core in the leftmost pattern (loop) is 12. A ridge tracing from left to right delta on the rightmost pattern (whorl) determines an inner tracing, meaning that when following a ridge emanating from the left delta, the ridge passes inside the other delta. [7]

At the local level, you find theminutiae details (sometimes called minutiae points). One way to classify the minutiae details are in terms of ridge termination, bifur-cation, independent ridge, dot or island, lake, spur, and crossover [7]. These are depicted in figure 3.3. The two most prominent minutiae details, are ridge termi-nation (ending) and ridge bifurcation [41].

Figure 3.3. Minutiae details, also known as ridge characteristics, ridge details, or Gal-ton’s details. Most of the identifications of fingerprints during this century, were made by matching corresponding minutiae details between two prints. [7]

18 Fingerprints

page 18. The position and shape of the pores can be used to help identify a person. To be able to use this information, a high-resolution image of the fingerprint is required. [41]

Figure 3.4. Part of a fingerprint image with sweat pores and minutiae details visible. The black lines in the image correspond to the ridges in the fingerprint, and the white lines in the image correspond to the valleys in the fingerprint. The white dots on the ridges correspond to the sweat pores in the fingerprint and are marked with empty circles on a single ridge line. Minutiae details are marked with black-filled circles. [41]

Figure 3.5 shows a cross-section of a papillary line. The sweat glands supply the papillary skin with moisture and when touching a surface with a finger, the sweat from these pores is transferred to the pattern of the fingerprint, see figure 3.4. The outer skin layer is called epidermis, and the inner skin layer is called dermis.

3.4 Enhancement techniques 19

3.4

Enhancement techniques

A latent fingerprint results from the reproduction of friction ridges found on fingers. To be able to identify the owner of the fingerprint, the fingerprint must in most cases first be enhanced in order for it to be visible. Enhancing a fingerprint will also be used in the experiments described in chapter 7 on page 55.

A print consists of a combination of different chemicals that originate from natural secretions, blood, and contaminants. Some contaminants found in fingerprints result from contact with different materials in the environment. [56]

Latent fingerprints can be found on all types of surfaces. In general, surfaces can be characterized as porous, nonporous, or semiporous. Understanding these characteristics helps in deciding the processing technique of the latent fingerprint. [56]

3.4.1

Processing techniques

In addition to the type of surface, another determining factor in choosing the proper process is the residue of the latent fingerprint, including perspiration, blood, oil or grease, and dust. [56]

The condition of the surface also contributes to determining the correct process. Such surface characteristics include dryness, wetness, dirtiness, and tackiness or stickiness. [56]

A variety of techniques, including use of chemicals, powders, lasers, alternate light sources, and other physical means, are employed in the detection and development of latent prints. For a detailed description of these different techniques and in which situations to use which techniques, see [56].

Two techniques will though be described more in detail here, since they have been found easily available for nonprofessionals and can be used on nonporous surfaces. Fingerprint powders

Powdering is the application of finely ground, colored powder to a nonporous object to make latent prints visible. Powder clings to moisture, oil, and other residues. [56]

Different colored powders can be used, e.g. black, white, and gray. The color of the powder depends on the surface, e.g. on a white surface, a black or gray powder will enhance the fingerprint much better than a white powder.

The recommended brushes to use with colored powders are fiberglass filament brushes, camel-hair brushes, feather dusters, and squirrel-hair brushes. [1, 56]

20 Fingerprints

A finely ground magnetic powder can also be used together with a magna brush wand. [56]

One important thing to take notice of when using powder and a brush, is to brush in the direction of any ridges that begin to appear. A detailed description of the procedure can be found on pages 26-27 in [56].

When the fingerprint has been powdered, the print has to be lifted in order to photograph it. If the fingerprint is already placed on a flat surface, you might not have to lift it, but can instead photograph it directly. When lifting the fingerprint, it is important to avoid air bubbles, which will easily form underneath the tape. Cyanoacrylate fuming

Cyanoacrylate fuming is also used to develop latent prints on nonporous specimens. This technique is not recommended to perform at home since it includes risks of getting allergic reactions and the fumes are life threatening. It should however be noticed that it is in fact possible to do at home with materials a nonprofessional can buy. Liquid cyanoacrylate can be found in adhesives available at most hobby shops.

Since cyanoacrylate fuming was only tried out in the experiments prior to this report, and not used to the same extent as powdering, it will not be described further here. A detailed description of the processing procedure can be found in [56] and a more amateur approach can be found in [20].

Chapter 4

Fingerprint scanners

Even though the first fingerprint scanners were introduced more than 30 years ago, it is not until the recent years that the interest for fingerprint scanning has increased considerably [41]. With the terrorist attack in New York on September 11, 2001, the US Government and other governments and organizations, became increasingly interested in the biometrics industry. Passport, border control, and identification cards are areas were fingerprints, as a means of authentication, have become in-creasingly interesting. The fingerprint scanner market has grown rapidly the last years. With this development, the scanners are shrinking in size, the price is going down, and fingerprint systems are being integrated into electronic equipment such as laptops, mouses, and keyboards.

A fingerprint scanner has basically two tasks; to acquire an image of a fingerprint, and to decide whether or not this image matches the image of a previously enrolled fingerprint. The decision phase is done by extracting features from the image and then comparing these features to templates stored in a database.

A fingerprint contains a lot of information. Storing and using all this information, would take too much space and unnecessary effort when a lot of the information in fact is redundant. Instead, fingerprint scanners focus on the essential information to make the fingerprint as unique as possible and thus useful in identification and verification situations. [3]

This chapter will describe the characteristics of a digital fingerprint image, the different scanning techniques used today, the algorithms behind the surface of the scanners, protection schemes, and possible ways of intrusion.

22 Fingerprint scanners

4.1

Fingerprint images

A digital fingerprint image can be characterized by the following main character-istics [41]:

• Resolution: The minimum resolution for FBI-compliant sensors are 500 dots per inch (dpi), and this is also met by many commercial devices. The sensors used in the extensive experiments have resolutions of 250 dpi and 380 dpi. • Area: The larger the area, the more ridges and valleys are captured, and

the more distinct the pattern becomes. The minimum area size required by FBI specifications is 1×1 square inches. Many sensors today have an area a lot smaller than that, thus making it impossible for the entire print to be captured. A small area keeps the cost and size down, but does also lead to unnecessary false rejections. The sensors used in the extensive experiments have area sizes of 9.8×9,8 mm, and 17×17 mm.

• Dynamic range (or depth): The number of bits used to encode the intensity value of each pixel. Grayscale is used and the FBI standard for pixel bit depth is 8 bits. Some sensors capture however only 2 or 3 bits of information. • Geometric accuracy: Can be defined as the maximum geometric distortion introduced by the acquisition device, and is expressed as a percentage with respect to x and y directions.

• Image quality: Difficult to measure, especially since it is hard to decouple it from the intrinsic finger quality or status.

All the characteristics mentioned above work together to set the accuracy of the system.

4.2

Scanning techniques

While the first generation scanners used optical techniques, a variety of sensing techniques are used today and almost all of them belong to one of the three families: optical, solid-state, and ultrasound. [41, 57]

The main technologies used today are optical and solid-state sensors (mainly capac-itive sensors). Solid-state sensors are now gaining great popularity because of their compact size which facilitates in embedding them into laptop computers, cellular phones, smart cards, and the like. [25, 41]

4.2.1

Optical sensors

The advantages with optical sensors include withstanding temperature fluctuations (to some degree), a fairly low cost, resolutions up to 500 dpi, better image quality,

4.2 Scanning techniques 23

and the possibility of larger sensing areas. [24, 41]

The drawbacks of optical sensors are size and problems with latent prints [24, 53, 55]. Cuts, abrasions, calluses, and other damage, as well as dirt, grease and other contamination, can also be a problem with optical scanners [29].

Frustrated Total Internal Reflection (FTIR)

When you place your finger on an FTIR-based optical sensor (see figure 4.1), the ridges will be in contact with the prism surface, while the valleys will remain at a distance. One side of the prism is illuminated through a diffuse light (a bank of light-emitting diodes (LED) or a film planar light). The light is reflected at the valleys and randomly scattered (absorbed) at the ridges. The lack of reflection from the ridges, makes it possible to acquire an image of the fingerprint. In the early days’ FTIR sensors, a CCD camera was used to acquire the fingerprint image. Today, the FTIR sensors have shrunk considerably in size and cost with help of the new CMOS technology. [41, 57]

Since FTIR devices sense a three-dimensional surface, it is difficult to fool them with a photograph or image of a fingerprint [41]. Latent prints are however still a problem [53, 55]. Furthermore, it is difficult to make a small enough FTIR device suitable to embed into a PDA or a mobile phone, even though they can be used in mouses and keyboards. [41]

Figure 4.1. An FTIR-based fingerprint sensor. [41]

FTIR with a sheet prism

This type of optical sensor, use a sheet prism made of a number of ”primlets” adjacent to each other, instead of a single large prism, see figure 4.2 on page 24.

24 Fingerprint scanners

With the advantage of size reduction, the quality of the acquired images is however lower than traditional FTIR techniques using glass prisms. [41]

Figure 4.2. A fingerprint sensor using FTIR with a sheet prism. [41]

Optical fibers

This technique uses a fiber-optic plate (see figure 4.3) instead of a prism and lens. The finger is in direct contact with the upper side of the plate, while the lower side of the plate is tightly coupled with a CCD or CMOS camera, which receives the light conveyed through the glass fibers. Since the CCD/CMOS is in direct contact with the plate (without any intermediate lens as in the FTIR techniques), its size has to cover the whole sensing area. High costs will thus be the downside of producing large area sensors with this technique. [41]

Figure 4.3.Fingerprint sensing using optical fibers. Residual light emitted by the finger, is conveyed through the glass fibers to the CCD/CMOS camera. [41]

Electro-optical

These type of sensors, consist of two layers: a light-emitting polymer, and a pho-todiode array, see figure 4.4 on page 25. When the polymer is polarized with the proper voltage, it emits light that depends on the potential applied on one side. As the ridges touch the surface, and the valleys do not, the potential, and thus

4.2 Scanning techniques 25

also the amount of light, will be different. The photodiode array (embedded in glass) receives the light and generates the digital fingerprint pattern. Some com-mercial sensors use the light-emitting polymer together with an ordinary lens and CMOS instead of the photodiode array. Images acquired electro-optically, are yet not comparable in quality with FTIR images. [41]

Figure 4.4. Electro-optical fingerprint sensor. [41]

Direct reading

A variation of optical sensors are the not so common touchless sensors. Instead of pressuring the finger against a plate, the finger is put on an area with a hole, about 2-3 inches from the optics behind. This technique may seem more hygienic, and saves time by not having to clean the sensor surface. It is however very difficult to obtain well-focused and high-contrast images. [21, 41]

4.2.2

Solid-state sensors

Solid-state sensors (also known as silicon sensors), were first introduced to overcome the problems with size and cost of optical sensors. However, considering a high-security device, a large sensing area is needed, and thus the cost will in fact not be any smaller for solid-state sensors than for optical sensors. [41]

All silicon sensors consist of an array of pixels, where each pixel is a tiny sensor itself. Four different types of silicon sensing techniques have been proposed to convert the physical information into electrical signals: capacitive, thermal, electric field, and piezoelectric. [41]

Capacitive sensors

A capacitive sensor consist of a two-dimensional array of micro-capacitor plates embedded in a chip, see figure 4.5 on page 26. The finger skin works as the other side of each micro-capacitor plate. This way, variations in electrical charge will appear due to distance variations from a ridge on the fingerprint to the sensor, and

26 Fingerprint scanners

from a valley on the fingerprint to the sensor. These small capacitance differences is then used to acquire an image of the fingerprint. [57]

Figure 4.5. Capacitive fingerprint sensor. [41]

Even though being widely used nowadays, capacitive sensors do have a number of disadvantages:

• Small sensor area: It can be questioned whether or not a small image scan area is enough to accurately identify an individual. The reduction in sensor size does also require more carefully performed enrollments. A poor enroll-ment may not capture the center of the fingerprint, thus forcing the subse-quent identification/verification fingers to be misplaced in the same way. The sensing area can of course be increased, however resulting in a higher cost. [24, 29, 41]

• Electrostatic discharge (ESD): Electrostatic discharges from the fingertip can cause large electric fields that could severely damage the device. [41] • Chemical corrosion: The silicon chip needs to be protected from chemical

substances (e.g. sodium) that are present in fingerprint perspiration. Pro-tecting the surface with a too thick coating will increase the distance between the pixels and the finger too much and make it more difficult to distinguish between a ridge and a valley. Therefore, the coating must be as thin as pos-sible, yet not too thin, or it will not be resistant to mechanical abrasion. [41]

Thermal sensors

Thermal sensors are made of pyro-electric material that generates current based on temperature differentials. The temperature differentials between the skin (the ridges) and the air (in the valleys) is used to acquire the fingerprint image. Since thermal equilibrium is reached quickly, it might be necessary to use a sweeping technique when it comes to thermal sensors. Thermal sensors are not sensitive to ESD, nor do they have any problems with a thick (10 to 20 microns) protective coating. [41]

4.2 Scanning techniques 27

Electric field sensors

The problems optical and capacitive sensor have with dry skin conditions, calluses, cuts, etc. is not the case of electric field sensors. These sensors enter the skin and creates a fingerprint image from below the damaged surface layer. The variations of the electric field is measured in the conductive layer, the boundary between the outer layer of damaged skin and the pristine skin. [4]

Piezoelectric (pressure)

The sensor surface is made of a non-conductive dielectric material. When pressure is applied by the finger, a small amount of current, dependent on the pressure, is generated (this effect is called the piezoelectric effect). The different pressure from the valleys and ridges, therefore result in different amounts of current. One of the disadvantages of this technique, is the materials used, which are often not sensitive enough to detect the differences between ridges and valleys. Additionally, the protective coating blurs the resulting image. [41]

4.2.3

Ultrasonic sensors

In an ultrasonic sensor (see figure 4.6), a transmitter sends acoustic signals toward the fingertip, and a receiver detects the echo signals which bounce off the fingerprint surface. The difference in acoustic impedance of the skin (ridges) and the air (valleys) is used to measure the distance, thus acquiring an image of the fingerprint. The frequency range used by these sensors, varies from 20 kilohertz to several Gigahertz. The top frequencies are required to get the required resolution to be able to differentiate fingerprints from each other. [41, 57]

Figure 4.6. An ultrasonic sensor uses sound waves which penetrate materials and give a partial echo at each impedance change. [41]

28 Fingerprint scanners

in accuracy rates approximately a factor of 10 better than any other fingerprint sensing technology on the market today. [29]

Except electric fields, ultrasound is one of the few technologies that images the subsurface of the finger skin, thus penetrating dirt, grease, etc. on the sensor surface and the finger. Ultrasound technology, though considered perhaps the most accurate of the fingerprint technologies, is not yet widely used due to large size and a quite high cost. Moreover, it takes a few seconds to acquire an image. [24, 41]

4.3

Touch versus sweep

Most sensors used today are touch sensors (area sensors). When using a touch sensor, you simply put your finger on the sensor and hold it for a few seconds without moving it. Very little user training is required to use a touch sensor. However, there are a few drawbacks with touch sensors as well:

• The sensor quickly becomes dirty and must be cleaned. Some users might have issues with using the device if it does not look clean. [41]

• Problems with latent prints exist. Depending on the type of sensing tech-nique, studies have shown that it is possible to reactivate a latent print on a fingerprint sensor. [53, 55]

• Rotation of the finger may be a problem for recognition. Some matching algorithms do not accept large rotations (e.g. more than 20 degrees) of the finger. [41]

• Tradeoff between cost and size of the sensing area. This is especially true for solid-state sensors, where the cost mainly depends on the area of the chip die. [41]

Because of these drawbacks, a new type of sensor was introduced: the sweeping sensor, see figure 4.7 on page 29. Sweeping sensors are as wide as a finger, but only a few pixels high. Therefore, the main advantage of sweeping sensors, especially in silicon sensors, is reduced cost. The sweeping consists of a vertical movement only. At the end of the swipe or ”on-the-fly”, the fingerprint image is reconstructed from all the images earlier acquired. [41]

The sweeping method was originally introduced in conjunction with thermal sors, but is nowadays used in many different types of sensors. Unlike touch sen-sors, sweeping sensors look clean since each user’s finger ”cleans” the sensor during sweeping. No problem with latent prints exist with sweeping sensors, and in most cases, rotation of the finger is neither a problem. Sweeping sensors do still have some drawbacks as well [41]:

4.4 Algorithms in fingerprint scanners 29

Figure 4.7. When a user is sweeping his/her finger on a sweeping sensor, a number of image slices are combined to form an image of the entire fingerprint. [41]

• Learning time. It takes a number of tries, before a user gets used to sweeping properly (i.e. without sharp speed changes, or discontinuity).

• The interface must be able to capture a sufficient number of fingerprint slices to follow the finger sweep speed.

• Reconstructing the fingerprint image from the slices is a time consuming process which usually produces errors.

4.4

Algorithms in fingerprint scanners

A typical fingerprint recognition system (see figure 4.8) consists of a scanning device (capture and enhancement), a feature extraction part, and a comparison part where an identification/verification decision is taken. This section will shortly describe these different parts in more detail.

30 Fingerprint scanners

4.4.1

Image enhancement

When a fingerprint image is captured, it contains a lot of redundant information. Problems with scars, too dry or too moist fingers, or incorrect pressure must also be overcome to get an acceptable image. Therefore, a number of filters, some of which will be described below, are applied to the image. [3]

• Normalization: By normalizing an image, the colors of the image are spread evenly throughout the gray scale. A normalized image is much easier to compare with other images, and the quality of the image is easier determined. [3]

• Binarization: Making an image binary, transforms the gray scale image into a binary image (black and white). Either a global or localized threshold value is used. [3]

• Low pass filtering: The process of low pass filtering smoothens the image to match the pixels nearby so that no points in the image differ from its surroundings to a great extent. By low pass filtering an image, errors and in-correct data are removed, and it simplifies the acquisition process of patterns or minutiae. [3, 27]

• Quality markup: Redundant information needs to be removed from the im-age before further analysis can be performed and specific features of the fingerprint can be extracted. Therefore segmentation, i.e. separating the fin-gerprint image from the background, is needed. Furthermore, any unwanted minutiae (can appear if the print is of bad quality) needs to be removed. [3, 27]

4.4.2

Feature extraction and comparison

Many algorithms have been developed to match two different fingerprints and they can be divided into the following groups:

• Minutiae-based matching: This is the most popular and widely used matching method, partly because it is the same technique as used by fingerprint exam-iners. As described in section 3.3.2 on page 16, a fingerprint pattern is full of minutiae points, which characterize the print. In minutiae-based match-ing, these points are extracted from the print, stored as sets of points in the two-dimensional plane, and then compared with the same points extracted during the enrollment phase. It is very unlikely that the fingerprint during enrollment and the fingerprint during identification/verification had the ex-act same angle, horizontal and vertical placement. Therefore, the core point (see section 3.3.2 on page 16), is used as a reference point for the coordinate system and the distance and angle from the core point is calculated and used

4.5 Sensor attacks and protection schemes 31

for each minutiae point. For identification/verification a certain number of minutiae points should match for the user to be successfully logged in. [3, 41] • Correlation-based matching: The fingerprint image to be identified/verified, is superimposed with the fingerprint image acquired during the enrollment. The correlation between corresponding pixels is computed for different alignments (e.g. various displacements and rotations). [41]

• Ridge feature-based matching: This matching method uses features of the ridge pattern, e.g. local orientation and frequency, ridge shape, and texture information. Even though minutiae-based matching is considered more reli-able because of its indistinctness, there are cases where ridge feature-based matching is better to use. In very low-quality fingerprint images, it can be difficult to extract the minutiae points, and using the ridge pattern for match-ing is then preferred. Ridge feature-based matchmatch-ing can be conceived as a superfamily of minutiae-based matching and correlation-based matching. [41]

4.5

Sensor attacks and protection schemes

A biometric system can be defeated in different ways, from attacks at the sensor level, to replay attacks on the data communication stream, and attacks on the template database [55]. This report focuses on the attack at the sensor level, which can be performed in a number of ways. Independent of the attack method, the false acceptance rate (FAR) and the false rejection rate (FRR), should be kept low.

4.5.1

Registered finger

The intruder can force the legitimate user to press his/her finger against the fin-gerprint sensor under duress [42]. Also, the intruder can give the legitimate user a sleeping drug, in order to use either the finger directly against the sensor, or by making a mold of the finger as described in [3, 42, 57].

Another way of using the registered finger, is by separating the finger from the legitimate user’s body [42]. This finger can then be used directly on the sensor, or by one of the methods described in [42].

To make it more difficult to attack a fingerprint system as described above, a fingerprint scanner can be combined with another authentication method, e.g. a PIN, a password, or an ID card. Another way to deter these crimes, is by using a way to alarm when under duress, e.g. with help of a special secret code or manner. Also, a two-persons control, where the system requires e.g. fingerprints from two different persons, would be helpful. Using a two-persons control is however very inefficient and not realizable in most situations. [42]

32 Fingerprint scanners

4.5.2

Unregistered finger

This attack means that the intruder uses his/her own finger to try (intentionally or unintentionally) to log in as another user. An important indication for how easy this type of attack is on a special system, is the false acceptance rate, described in section 2.1.2 on page 9. Also, by knowing the pattern type of the legitimate user, an intruder with the same pattern type (see section 3.3.1 on page 16) will have a higher probability of successfully logging in as the legitimate user. [42]

4.5.3

A twin’s fingerprint or a genetic clone

As described in section 3.2 on page 14, the fingerprints of identical twins are very similar, even though not identical. Using a genetic clone of a fingerprint or the identical twin’s fingerprints to deceive a system could be possible if the algorithm used is not robust enough to distinguish the live finger from the intruder’s finger. The attack with a genetic cloned finger could be detected with help of a liveness detection mechanism in the system. Using a combination with another authenti-cation method, or using a two-persons control, would also be helpful to deter these crimes. However, protection against the identical twin is not as easy as protection against a genetic clone. [42]

4.5.4

Artificial fingerprint

An artificial fingerprint, is a fingerprint made to imitate a real (living) fingerprint. It can be made of gelatin, silicone, play-doh, clay, or other materials. There are two ways to make an artificial fingerprint; either by directly making a mold of the legitimate user’s finger, or by using a residual fingerprint to produce an artificial fingerprint [42]. The experiments prior to this report, focus on attacks with artificial fingerprints made from a residual fingerprint.

Again, liveness detection in the system, a combination with another authentication method, or a two-persons control would be helpful to deter these crimes. [42]

4.5.5

Others

Some fingerprint systems can be fooled by flashing a light against the scanner, heating up, cooling down, humidifying, impacting on, or vibrating the scanner outside its environmental tolerances. Another way to fool a fingerprint system is to use a residual fingerprint on the sensor surface to reactivate the fingerprint. This can be done by breathing on the sensor’s surface, placing a thin-walled water-filled plastic bag on the sensor’s surface, or by dusting graphite powder and then pressing an adhesive film on the sensor’s surface. On an optical sensor, the method with

4.5 Sensor attacks and protection schemes 33

graphite powder and adhesive film can be used together with a halogen lamp to create a kind of snow blindness in the sensor. [42, 55]

To protect against attacks using reactivation of a latent fingerprint, a sweeping sensor can be used instead of an area sensor.

Chapter 5

Liveness detection

Liveness detection (sometimes called vitality detection) in