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Fingerprint Image Reconstructionfrom Standard Templates

Raffaele Cappelli, Alessandra Lumini, Dario Maio, Member, IEEE, and Davide Maltoni, Member, IEEE

Abstract—A minutiae-based template is a very compact representation of a fingerprint image, and for a long time, it has been assumed

that it did not contain enough information to allow the reconstruction of the original fingerprint. This work proposes a novel approach to

reconstruct fingerprint images from standard templates and investigates to what extent the reconstructed images are similar to the original

ones (that is, those the templates were extracted from). The efficacy of the reconstruction technique has been assessed by estimating the

success chances of a masquerade attack against nine different fingerprint recognition algorithms. The experimental results show that the

reconstructed images are very realistic and that, although it is unlikely that they can fool a human expert, there is a high chance to deceive

state-of-the-art commercial fingerprint recognition systems.

Index Terms—Biometric systems, security, ISO/IEC 19794-2 fingerprint standard template, fingerprint reconstruction, minutiae.

Ç

1 INTRODUCTION

FINGERPRINT-BASED biometric systems are rapidly gainingacceptance as one of the most effective technologies to

authenticate users in a wide range of applications: from PClogon to physical access control and from border crossing tovoters authentication [22]. A typical fingerprint verificationsystem involves two stages: during enrollment, the user’sfingerprint is acquired and its distinctive features areextracted and stored as a template; and during verification, anew fingerprint is acquired and compared to the storedtemplate to verify the user’s claimed identity. The distinctivefeatures used by most fingerprint-based systems are the so-called minutiae, which are local characteristics of the patternthat are stable and robust to fingerprint impression condi-tions [22]. With the aim of achieving interoperability amongdifferent fingerprint-based recognition systems [24], aninternational standard for minutiae template representationhas been recently defined as ISO/IEC 19794-2 [17], which is aminor modification of the earlier ANSI-INCITS 378-2004 [16].

Since a template based on minutiae is a very compactrepresentation of the fingerprint, many researchers andpractitioners in the biometric field postulated that a templatedoes not include enough information to reconstruct theoriginal fingerprint image (that is, the template extractionprocedure has been traditionally considered a one-wayfunction). However, this belief was recently questioned by afew works [13], [30] that explored the reversibility of minutiaetemplates and suggested the possibility of a masquerade attack,that is, using a fingerprint image reconstructed from atemplate for spoofing a recognition system by manufacturing

a fake finger [23], [36] or directly injecting the digital imageinto a communication channel [28].

Considering the growing diffusion of fingerprint-basedsystems in large-scale applications (for example, electronicidentity documents and border crossing [12]) and the effortstoward interoperability (which is certainly desirable), it isdefinitely urgent to carefully investigate to what extent animage reconstructed from a template may be similar to theoriginal fingerprint. In particular, it is important to under-stand whether such a reconstructed fingerprint image canfool 1) a human expert examiner and 2) an automaticrecognition system through a masquerade attack.

Theresultsofsucharesearcharecertainlyrelevant toall thecurrent applications and case studies where standardtemplate storage and interoperability play an important rolesuch as the PIV program [25], where ANSI-INCITS 378-2004templates are stored in clear on smart cards, and the ILOSeafarers’ IdentityDocument[15],wheretheISO/IEC19794-2templates are printed in clear on plastic cards as 2D barcodes.

This work introduces a novel approach to reconstructfingerprint images starting from minutiae data stored asISO/IEC 19794-2 templates [17]. The proposed technique isbased on a sequence of steps: Starting from the informationavailable in the template, attempt to estimate variousaspects of the original unknown fingerprint—the patternarea, the orientation image, and the ridge pattern; then arendering step is finally executed to make the reconstructedfingerprint more realistic. Although specifically designed totake ISO standard templates as input, this approach may beeasily adapted to work with any kind of minutiae-basedtemplate (including ANSI-INCITS 378-2004 [16]). Theefficacy of the proposed approach has been assessed byestimating the success chances of a masquerade attackagainst nine different fingerprint recognition algorithms.

The rest of the paper is organized as follows: Section 2briefly summarizes the previous literature and highlights thenovelty of the proposed technique. Section 3 contains basicnotation and definitions related to fingerprint analysis anddescribes the ISO/IEC 19794-2 standard. Section 4 explainsthe various steps of the new reconstruction approach.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 9, SEPTEMBER 2007 1489

. R. Cappelli, A. Lumini, and D. Maltoni are with the Scienzedell’Informazione, Universita di Bologna, via Sacchi 3, 47023 Cesena(FO), Italy. E-mail: {cappelli, lumini, maltoni}@csr.unibo.it.

. D. Maio is with the Department of Electronics, Informatics, and Systems,CSITE CNR, Universita di Bologna, viale Risorgimento 2, 40136 Bologna,Italy. E-mail: [email protected].

Manuscript received 19 May 2006; revised 26 Oct. 2006; accepted 8 Dec.2006; published online 18 Jan. 2007.Recommended for acceptance by H. Wechsler.For information on obtaining reprints of this article, please send e-mail to:[email protected], and reference IEEECS Log Number TPAMI-0386-0506.Digital Object Identifier no. 10.1109/TPAMI.2007.1087.

0162-8828/07/$25.00 � 2007 IEEE Published by the IEEE Computer Society

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Section 5 reports both qualitative and quantitative experi-mental results. Finally, Section 6 draws some conclusions.

2 RELATED WORKS

The potential weaknesses of biometric systems have beenanalyzed in [28], [29], where the possible attacks areclassified into four distinct categories:

. attacking the communication channels, includingreplay attacks on the channel between the sensorand the rest of the system,

. attacking specific software modules (for example,replacing the feature extractor or the matcher with aTrojan horse),

. attacking the database of enrolled templates, and

. presenting fake biometrics to the acquisition sensor.

Recently, the research on the last type of attack has beenparticularly active in the fingerprint domain. Several experi-ments aimed at investigating how current fingerprint-basedtechnologies may be spoofed by fake fingers have beenperformed by various research groups [6], [18], [23], [27], [5][2], [3], [32], [36]; all of them concluded that, nowadays, nocommercial fingerprint scanner seems to be totally resistantto fake fingers made with the appropriate materials (forexample, gelatin, silicone, and latex) and proper techniques.

The regeneration of biometric samples (or at least of theirmain discriminant features), with the aim of attacking arecognition system, has been discussed in various worksconsidering different modalities: brute force attempts [29],hill-climbing attacks [1], [33], and reconstruction fromtemplates. The last modality received less attention in thepast, probably because the nonreversibility belief was quitewidespread [14]. However, some pioneering works haverecently addressed this issue in the fingerprint domain [13],[30] by proposing techniques to estimate the fingerprint class,the orientation image, and to draw a feasible pattern startingfrom the minutiae data. In [13], a neural network classifier isadopted to predict the fingerprint class; the orientation modelproposed in [31] is used to estimate the orientation image, andthe reconstructed pattern is created by a heuristic line-tracingapproach. The approach reported in [30] estimates theorientation image starting from the minutiae triplets, infersthe fingerprint class using a k-nearest neighbor classifier, andgenerates the final pattern by means of Gabor filters. Thesetwo works represent a first step toward the disproof of the

nonreversibility belief and suggest that the informationcontained in fingerprint templates may allow images some-what similar to the original fingerprints to be reconstructed.Anyway, the results achieved appear still limited: In [13], thereconstruction is performed only on fingerprints with a verysimple pattern (those belonging to the arch class); thevalidation in [30] is mainly qualitative, and in both the aboveworks, the visual quality of the reconstructed image is notmuch satisfactory.

This work addresses the same problem discussed in [13]and [30], but it substantially differentiates in

1. the template format (ISO/IEC 19794-2) on which thereversibility study is performed,

2. the mathematical models and estimation techniqueson which the reconstruction approach is based,

3. the visual quality of the reconstructed fingerprints,and

4. the systematic validation of the new technique againstnine different fingerprint matching algorithms.

The novel reconstruction technique was briefly introducedin an earlier work [8], where some examples of thereconstructed images are shown and visually compared tothose reported in [13] and [30]; in this work, the wholeapproach is described, and the results of systematicexperiments are reported.

3 FINGERPRINT PATTERNS AND THE

ISO TEMPLATE

3.1 Fingerprint Anatomy

A fingerprint is the reproduction of a fingertip epidermis,which is produced when a finger is pressed against a flatsurface. The main structural characteristic of a fingerprint is apattern of interleaved ridges (also called ridgelines) andvalleys (see Fig. 1a), which often run in parallel. At a globallevel, fingerprint patterns usually exhibit one or more regionswhere the ridgelines assume particular shapes (characterizedby high curvature, frequent terminations, and so forth). Theseregions (called singularities or singular regions) may beclassified into three types: loop, delta, and whorl (see Fig. 1b).Singular regions belonging to loop, delta, and whorl types areusually characterized by \, �, and O shapes, respectively.Singular regions are commonly used for fingerprint classifi-cation [22] (see Fig. 2), that is, assigning a fingerprint to a class

1490 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 29, NO. 9, SEPTEMBER 2007

Fig. 1. (a) Ridges and valleys on a fingerprint image. (b) Singular regions (white boxes) and core points (small circles) in fingerprint images.


among a set of distinct classes, with the aim of simplifyingsearch and retrieval.

Fig. 2 shows the five most common classes of the Galton-Henry classification scheme [22] (Arch, Tented arch, Left loop,Right loop, and Whorl):

. Arch fingerprints have ridges that enter from oneside, rise to a small bump, and go out the oppositeside: no singularity is present.

. Tented arch fingerprints are similar to the arch, exceptthat some ridgelines exhibit a high curvature, andthere are only one loop and one delta (usuallyvertically aligned).

. Left (right) loop fingerprints have one or moreridges that enter from the left (right) side, curveback, and exit from the same side they entered; aloop and a delta singularity are present: The loop istypically located on the left (right) side of the deltawith respect to a vertical axis.

. Whorl fingerprints contain two loop singularities (ora single whorl, which may be considered as twoopposite loops at the same location) and two deltasingularities; the whorl class is the most complex,and in some classification schemes, it is furtherdivided into some subclasses.

Several fingerprint matching algorithms prealign finger-print images according to a center point (core), typicallydefined as the position of the northmost loop singularity oras the point of maximum ridgeline curvature for finger-prints belonging to the arch class (Fig. 1b).

The ridgeline pattern may be effectively described by theorientation image, which is a discrete matrix whose elementsdenote the local orientation of the ridgelines (Fig. 3b). Thegeneric element ½x; y�of the orientation image is defined as the

angle �xy that the tangent to the fingerprint ridges in thecorresponding local neighborhood of the image forms withthe horizontal axis (Fig. 4). Analogously, the local ridgelinefrequency (defined as the number of ridges per unit length)may be effectively represented by using a frequency image(Fig. 3c).

At a finer level, other important features called minutiaecan be found. Minutiae are ridgeline discontinuities and maybe classified into several types [22]: termination, bifurcation,island, dot, lake, and so forth. However, to deal with thepractical difficulty in automatically discerning them withhigh accuracy, usually only a coarse classification into twotypes is adopted (Fig. 5): termination (the point where a ridgesuddenly ends) and bifurcation (the point where a ridgedivides into two ridges). A minutia point may be defined byits type, the x and y-coordinates and the direction � (Figs. 5aand 5b).

Thanks to their stability and high discriminant power,minutiae are the most commonly adopted feature forfingerprint matching [22]. At a very fine level, other detailscan be also detected: These are essentially the finger sweatpores whose position and shape are considered to be highlydistinctive; however, extracting pores is feasible only onhigh-resolution fingerprint images (for example, 1,000 dotsper inch (dpi)) with a very high quality and, therefore, thesekinds of features are not adopted in practice.

3.2 Fingerprint ISO Template

The ISO/IEC 19794-2:2005 standard [17] specifies dataformats for minutiae-based fingerprint representation. Itdefines a generic record format that may include one ormore templates from one or more finger impressions, and itis designed to be used in a wide range of applications where

CAPPELLI ET AL.: FINGERPRINT IMAGE RECONSTRUCTION FROM STANDARD TEMPLATES 1491

Fig. 2. The five commonly used fingerprint classes: the positions of the singularities are graphically marked.

Fig. 3. (b) Orientation image and (c) frequency image of (a) fingerprint,

lighter blocks in the frequency image denote regions with a higher

frequency.

Fig. 4. A fingerprint image faded into the corresponding orientation

image.


automated fingerprint recognition is involved. The stan-dard defines the relevant terms, describes how to determineminutiae type, position, and orientation, and specifies theformats to be adopted for storing the data. In particular,three different data formats are defined: A record-basedformat, which is named Fingerprint Minutiae Record Formatfor general fingerprint template usage and interoperability,and two formats (normal and compact) for smart cards orother tokens. In this work, only the record-based format hasbeen considered for all the tests since it is the most generaltemplate representation available in the standard; anyway,the proposed reconstruction approach may be applied tothe other formats without any significant modification.

The Fingerprint Minutiae Record Format defines thefundamental data elements used for minutiae-based repre-sentation of a fingerprint and optional extended data formatsfor including additional data such as ridge counts and

singularities location. Table 1 summarizes the structure of the

records and the main fields (including all those relevant to the

reconstruction approach introduced in this work). A Finger-

print Minutiae Record contains a Record Header that includes

general information (for example, the image size) and the

number of fingerprints (Finger Views) represented. For each

Finger View, the corresponding Single Finger Record contains

minutiae data (mandatory) and extended data (optional).For each minutia, the corresponding Finger Minutia

Record (6 bytes) contains

. the minutia type (termination, bifurcation, or other),where “other” is defined as a minutia type thatmay be matched with all the types (hence, it maydenote both an unknown type or a type other thantermination/bifurcation),


Fig. 5. (a) A termination minutia, where ðx; yÞ are the minutia coordinates, � is defined as the mean direction of the tangents to the two valleys

enclosing the termination and is measured increasingly counterclockwise from the horizontal axis to the right. (b) A bifurcation minutia, where � is

defined as the mean direction of the tangents to the two ridgelines enclosing the ending valley and is measured increasingly counterclockwise from

the horizontal axis to the right. (c) Terminations (white) and bifurcations (gray) in a sample fingerprint.

TABLE 1The ISO/IEC 19794-2:2005 Fingerprint Minutiae Record Format


. the minutia x and y position expressed in pixels withrespect to the coordinate system in Fig. 5,

. the minutia direction � measured as explained inFig. 5 and recorded as a single byte in units of1.40625 (360/256) degrees, and

. the minutia quality in the range 1 (minimum quality)to 100 (maximum quality), or 0 if no qualityinformation is provided.

The Extended Data is designed for containing additional

information that may be used by the matching algorithm.

More than one Extended Data Record may be present for each

Finger View. For each record, the Extended Data Area Type

Code denotes whether the data is in a vendor internal format

or in one of the following formats defined in the standard:

. Ridge Count Data Format—optional informationabout the number of fingerprint ridges betweenpairs of minutiae,

. Core and Delta Data Format—optional informationabout the placement and characteristics of the loop/whorl1 and delta singularities (see Section 3.1) on theoriginal fingerprint image, and

. Zonal Quality Data—optional information about thequality of the fingerprint image within each cell in agrid defined on the original fingerprint image.

The approach proposed in this work is able to reconstruct a

fingerprint image starting from the minutiae template

obtained from a single fingerprint; therefore, for simplicity,

Fingerprint Minutiae Records containing only one Finger

View will be considered, but the approach could be easilyapplied to templates containing more Finger Views.

The following record fields are used to reconstruct the

fingerprint image:

. Image Horizontal and Vertical Size,

. Image Resolution (for a typical fingerprint scanner, itis reasonable to assume the same horizontal andvertical resolution),

. Number of Minutiae n,

. Type, Position x; y, and Direction � of each minutia,and

. Core and Delta positions (if provided in the ExtendedData).

4 THE RECONSTRUCTION APPROACH

LetW andH be the horizontal and vertical sizeof the image, asspecified in the template, respectively, letR be the resolutionof the image (in pixel per cm), and let M ¼ fm1;m2; . . . ;mngbe the set ofnminutiae in the template, where each minutia isdefined as a quadruple mi ¼ fti; xi; yi; �ig that indicates itstype ðti 2 ftermination; bifurcation; othergÞ, position xi, yi (inpixels), and direction �i (0 � �i < 2�, converted in radiansfrom the discrete byte value in the template).

The reconstruction approach is based on a sequence ofsteps that, starting from the information available in thetemplate, attempt to estimate various aspects of the originalunknown fingerprint (Fig. 6): the fingerprint area, theorientation image, and the ridge pattern. A final renderingstep is executed to make the reconstructed fingerprintimage more realistic. The following sections describe thereconstruction steps and the related mathematical models.


Fig. 6. A functional schema of the proposed reconstruction approach.

1. The standard does not distinguish between loop and whorlsingularities and simply names them as “cores.”


4.1 Fingerprint Area

Depending on the finger size, position, and pressure againstthe acquisition sensor, acquired fingerprint images havedifferent sizes and external shapes; this obviously affects thenumber and position of the minutiae reported in thetemplate. A simple but effective mathematical model forthe fingerprint area has been introduced in [7]: The model,based on four elliptical arcs and a rectangle, is controlled by

five parameters ða1; a2; b1; b2; cÞ to define the external shape

of the area and by two parameters ðxo; yoÞ defining the

center (see Fig. 7).In order to estimate the minimal area enclosing all the

minutiae in the template, a greedy heuristic algorithm has

been adopted to find reasonable values for the model

parameters (Fig. 8). The algorithm chooses the center as the

centroid of the minutiae set and fixes the value of parameter c

toR=6, which makes the initial shape slightly elongated in the

vertical direction, then, starting from an area containing most

of the minutiae, iteratively enlarges it until all the minutiae

are enclosed. Finally, the shape is slightly enlarged on all the

sides (adding a fixed offset ofR=60) to guarantee a minimum

border around the most external minutiae.The experimental results (see Fig. 9 for some examples)

showed that the area model is appropriate, its degrees of

freedom allow the typical fingerprint area shapes to be

covered, and the optimization approach is effective. The

algorithm is also very fast, being the number of iterations

comparable to the number of minutiae. It is worth noting

that the model assumes no significant rotation of the

fingerprint with respect to the vertical axis; since templates

obtained during enrollment have usually a proper position-

ing, it has not been considered convenient adding this

further degree of freedom.


Fig. 7. The fingerprint area model.

Fig. 8. Pseudocode of the greedy algorithm used to derive appropriate parameters for the model. Function FindMinArcðÞ finds the semiaxes of the

elliptical arc with a minimum area that contains mj: Since minutiae positions and semiaxes are discretized in pixels, a simple exhaustive search is

performed.

Fig. 9. Fingerprint area as estimated for some minutiae sets.


4.2 Orientation Image

The orientation image defines the overall ridgeline flow thatforms the fingerprint pattern; hence, in order to achieve agood reconstruction of the fingerprint, it is crucial to derivean orientation image as much similar as possible to the realone. The only information contained in the ISO template thatmay help to infer the orientation image are the directions � ofeach minutia (see Section 3.2): From the set M of minutiae, it isthen possible to obtain partial sparse information on theorientation image (Fig. 6). Deriving a complete orientationimage from such partial information is a challenging task: In[30], sets of minutiae triplets are used to estimate the localorientation in triangular fingerprint regions, and a final localaveraging step is performed to obtain a smooth result. In thiswork, in order to make the orientation estimation robust evenif the number of minutiae is small or they do not uniformlycover the fingerprint area, a model-based approach isproposed, where some parameters are optimized to fit theminutiae directions in the template.

The orientation model adopted in this work was originallyproposed in [34] and extended in [7] to enable the generationof synthetic orientation images. This particular model hasbeen considered better suited to this optimization task amongthe various ones proposed in the literature [31], [34], [4] [37],[19] since it is able to effectively represent most of theorientation patterns with a small number of parameters. Thelocal orientation is defined as a function of

. the number ns of loop and delta singularities: Notethat, in the typical fingerprint classes, the number ofloop and delta singularities is always the same (seeSection 3.1),

. the locations of the loop and delta singularities: lj ¼½lxj; lyj�T and dj ¼ ½dxj; dyj�T, j ¼ 1 . . .ns, and

. other parameters whose number and meaning varyaccording to ns (as detailed below).

The orientation�xy at a given point z ¼ ½x; y�T is defined as

�xy¼

arctan max 0;karch�3�yHf g�cos xW�ð Þð Þ if ns¼0 ðarch classÞ

12

Pnsj¼1

gdj arg z�djð Þð Þ�Pnsj¼1

glj arg z�ljð Þð Þ� �

if ns¼1 or ns¼2;

8<:

where

. the function argðpÞ returns the phase angle of vectorp (treated as a complex number),

. karch is a parameter controlling the curvature for archclass fingerprints, and

. gsð�Þ, with s 2 fl1; l2; d1; d2g, are piecewise linearfunctions defined as

gsð�Þ ¼ gsðqÞ

þ 4�

�� q

� �gs ðq þ 1Þmod 8ð Þ � gsðqÞð Þ; q ¼ 4�

�

� �:

Each function gsð�Þ is defined by the eight control points

gsðqÞjq ¼ 0 . . . 7f g, which set the value of the function for

fixed � angles ðq�4 ; q ¼ 0 . . . 7Þ. Fig. 10 shows the effect of

modifying some of the control points gsðqÞ on a sample

orientation image.Fig. 11 shows an example of orientation image generated

by the above model for each of the five fingerprint classes

(see Section 3.1).


Fig. 10. The effect of modifying some of the control points gl1 ðqÞ that define the values of the piecewise linear function gl1 ð�Þ corresponding to the

loop singularity of an orientation image obtained with ns ¼ 1; in (b), the orientation differences with respect to (a) are highlighted.

Fig. 11. Examples of orientation images corresponding to the five fingerprint classes obtained with the orientation model adopted.


The set P of unknown parameters that have to be estimateddepends on the number of singularities, in particular,

P ¼fkarchg if ns ¼ 0fl1;d1; ul1 ; vl1 ; ud1

; vd1g if ns ¼ 1

fl1; l2;d1;d2; ul1 ; vl1 ; ul2 ; vl2 ; ud1; vd1

; ud2; vd2g if ns ¼ 2:

8<:

To reduce the model complexity, for each piecewiselinear function gsð�Þ, P contains just two parameters us andvs: In fact, the eight control points gsðqÞ for each singularitys are derived from us and vs according to Table 2.

Table 3 reports the ranges of variations for the modelparameters.

To make explicit the dependency of the orientationimage on the parameters in P, the orientation at a givenpoint ½x; y�T will be denoted here as �xyðPÞ.

The estimation of optimal values for the parameters isthen carried out through an optimization process byminimizing the cost function fcostðPÞ

fcostðPÞ ¼1

n

Xni¼1

orientDiff��i mod �; �xiyiðPÞ

;

where �i is the direction of the ith minutia in the

template, �xiyiðPÞ is the orientation given by the orienta-

tion model at the coordinates ½xi; yi�T of the ith minutia,

and orientDiffð’1; ’2Þ 2 0; �2 �

evaluates the absolute dif-

ference between two orientations:

orientDiffð’1; ’2Þ ¼min j’1 � ’2j; �� j’1 � ’2jf g; ’1; ’2 2 ½0; �Þ:

Three independent minimizations are performed (one foreach value of ns 2 f0; 1; 2g) by using a direct searchalgorithm: The Nelder-Mead simplex [26]. This particularalgorithm has been chosen since it employs only functionevaluations and does not rely on derivative information,which would be difficult to calculate due to the complexity ofthe cost function. The overall minimum among the threens values is chosen and the corresponding orientation imageis finally postprocessed in order to better fit the minutiaeorientations. For each minutia i, the postprocessing consistsof replacing �xiyi with the original minutiae orientationð�i mod �Þ and then locally averaging the orientations in asmall neighborhood to produce a smooth result.

Fig. 12 compares some reconstructed orientation imageswith the orientation images calculated from the originalfingerprints using the approach proposed in [10].

In case the ISO template contains information about theposition of the singularities in the Extended Data (seeSection 3.2), the above optimization is simpler; in fact, it iscarried out with a fixed value for ns and with a reduced set ofparameters, because estimating the singularity positions ljand dj is no longer necessary.

4.3 Ridge Pattern

The global characteristics of the ridge pattern may bedescribed by the orientation image and the frequency image(see Section 3.1). Unfortunately, local frequency informationis not among the mandatory data required by the ISOtemplate; in case the template contains ridge count informa-tion in the Extended Data, an effective technique may bebased on the interpolation of the ridge count values betweendifferent pairs of minutiae; however, such an additionalinvestigation is outside the aims of this work. On the otherhand, reconstructing a frequency image from the minutiaeinformation seems to be almost impracticable: under somesimplifying hypothesis, one may try to infer something fromthe relative position of minutiae pairs and the singularitypositions, but usually, the number of minutiae is too low tocome to any robust conclusion. For this reason, the approachhere proposed simply assumes a constant frequency � forthe whole fingerprint and, instead of attempting to estimateit, reconstructs four fingerprint images with differentfrequency values in a range determined according to theimage resolutionR: � ¼ 2:54

500 R � T� �1

, with period T ¼ 6, 7, 8,9 pixels.2 This range of variation allows to cover typicalridgeline frequencies in nature [22]; adding intermediatevalues did not lead to better results in our preliminary testson 500 dpi images.

Given the minutiae set M, the estimated orientationimage ½�xy�, and the frequency �, the ridge patternreconstruction involves the following steps:

1. minutiae prototype positioning and2. iterative pattern growing.

Step 1 starts from an empty image and, for each minutiami ¼ fti; xi; yi; �ig in M, places at position ðxi; yiÞ a smallprototype (that is, a small raster image resembling thecharacteristics of a minutia) corresponding to minutia typeti, scaled according to � and rotated according to

~�i ¼�xiyi if min j�xiyi � �ij; 2�� j�xiyi � �ij

� < �

2�xiyi þ � otherwise;

�


TABLE 2Control Points gsðqÞ as Functions of us and vs

for the Different Types of Singularities s

2. In a 500-dpi image, parameter T is exactly the ridgeline period inpixels.

TABLE 3Ranges of Variation for the Model Parameters


where angle ~�i has the estimated orientation �xiyi and thedirection closer to the minutia direction �i.

Fig. 13 shows the two prototypes (bifurcation andtermination) and some examples of the result of Step 1 fordifferent frequencies �. In case ti ¼ other (minutiae typeunknown or not reported), the bifurcation prototype is used.

Step 2 iteratively grows the minutiae prototypes byapplying at each pixel ðx; yÞ a Gabor filter adjustedaccording to the frequency � and the local orientation �xy:

gaborðr; s : �xy; �Þ ¼ e�ðrþsÞ2

2�2 � cos 2��ðr sin�xy þ s cos�xyÞ �

:

The parameter �, which determines the bandwidth of thefilter, is adjusted according to the frequency so that the filterdoes not contain more than three effective peaks (see Fig. 14).

This pattern growing technique is analogous to theapproach proposed in [7] for the generation of syntheticridgeline patterns: At each iteration, the application of theGabor filters makes the nonempty regions in the imagegrow (Fig. 15) until they merge generating a uniformridgeline pattern; the process terminates when the wholeimage has been covered. An efficient implementation of the

filtering can be achieved by using separable Gabor filters,

which can be precomputed according to a discretized set of

frequencies (4) and orientations (256).

4.4 Rendering

The output of the previous step is a “perfect” pattern with

black ridges and white valleys; the noising and rendering

step proposed in [7] is finally applied in order to


Fig. 12. Four examples of reconstructed orientation images: for each of them, the original orientation image is shown on the left and thereconstructed one on the right; the minutiae orientations are superimposed to both. The orientation images have been obtained starting only fromminutiae positions and orientations.

Fig. 13. From left to right: A set of minutiae, the two prototypes (bifurcation and termination), and the four results of Step 1 for different frequencies� ðT ¼ 6; 7; 8; 9Þ.

Fig. 14. An example of the Gabor filter used in Step 2: Note that the

bandwidth is adjusted so that the filter does not contain more than three

peaks.


. make the images more realistic to the human eye,

. avoid potential problems that matching algorithmsmay have when processing images with very sharpedges, and

. prevent automatic algorithms to reject reconstructedimages by simply detecting the absence of noise.

The rendering step is aimed at simulating some of thefactors that contribute to deteriorate the quality of realfingerprints, thus producing gray-scale noisy images:irregularity of the ridges and their different contact withthe sensor surface, presence of small pores within theridges, presence of very small prominence ridges, gaps, andcluttering noise due to nonuniform pressure of the fingeragainst the sensor, and so forth. The noising and renderingstep sequentially performs the following operations:

1. Isolate the valley white pixels into a separate layer.2. Add noise in the form of small white blobs of variable

size and shape. The amount of noise increases with theinverse of distance from the boundary of the finger-print area (estimated as explained in Section 4.1).

3. Smooth the resulting image with a 3� 3 averagingbox filter.

4. Superimpose the valley layer to the image obtained.5. Remove any part of the pattern lying outside the

fingerprint area.

Fig. 16 shows two examples of the noising and renderingstep. It is worth noting that the image background iscompletely white: this imitates the output of a clean opticalsensor such as the one used in the experimentation (seeFig. 17); the background-generation approach proposed in[7] may be used to add sensor-specific backgrounds.


Fig. 15. An example of Step 2 of the ridge pattern reconstruction.

Fig. 16. Two examples of the noising and rendering step.

Fig. 17. Sample fingerprint images from the data set used in the experimentation.


5 EXPERIMENTAL RESULTS

The proposed fingerprint reconstruction approach has beenevaluated on fingerprint images (388� 374 pixels) acquiredthrough a 500 dpi optical scanner during the collection ofSecond International Competition for Fingerprint Verifica-tion Algorithms (FVC 2002) database 1 (DB1) [11]. FVC2002data collection involved 30 volunteers in three differentsessions [21]: During the first one, the volunteers were askedto place their fingers over the acquisition sensor “naturally,”whereas during the other two, specific perturbations wereadded (exaggerated displacement and rotation, and mois-tened and dried fingers). For each volunteer, fingerprintsfrom four different fingers (forefinger and middle finger ofeach hand) were acquired. In order to create a data setcontaining fingerprint images as much similar as possible tothose typically acquired during the enrollment stage of ageneric application, the first impression of each fingeracquired at the first session has been selected, thus obtaining120 different fingerprints. Fig. 17 shows some samplefingerprints from the data set. For each fingerprint, anoptimized version of the minutiae extraction algorithmdescribed in [20] has been adopted to create a correspondingISO template, which has been used as input for reconstruct-ing four fingerprint images (with different frequency values;see Section 4.3) using the proposed approach.

In Section 5.1, some reconstructed fingerprint images arecompared to the original ones and visually analyzed, andthen the results are discussed. In Section 5.2, the effectivenessof the proposed approach is validated by simulating attacksagainst state-of-the-art fingerprint matching algorithms.

5.1 Examples of Reconstructed Images

Fig. 18 compares an original fingerprint with one recon-structed from the corresponding ISO template: The simi-larity between the two images is high. The two ridgelinepatterns are extremely close in most of the common area, asit may be observed in the overlay image. It is worth notingthat the reconstruction has been performed starting onlyfrom the minutiae data; no additional information has beenconsidered (for example, position of the singularities). Thismeans that the orientation model and the optimizationprocedure introduced in Section 4.2 have been able toproperly reconstruct the orientation image.

A further analysis shows that the results are good also ata minutiae level: Most of the original minutiae are presentin the reconstructed image with the correct position andorientation, although the reconstructed image often con-tains more minutiae than the original one. Figs. 19 and 20report two examples where the minutiae in the core regionhave been manually marked and matched. In Fig. 19, thenumber of matching minutiae is eight (over nine in the


Fig. 18. From left to right: an original fingerprint, a reconstructed fingerprint from the corresponding ISO template, and an overlay of the two images.

An animation that better highlights the similarities between an original and a reconstructed image is available in the supplemental material, which can

be found at http://computer.org/tpami/archives.htm.

Fig. 19. The comparison of an original and a reconstructed image: minutiae in the core region have been manually marked (circles and squares

denote matching and nonmatching minutiae, respectively).


original fingerprint and 14 in the reconstructed one); inFig. 20, the number of matching minutiae is three (over fourin the original fingerprint and 13 in the reconstructed one).The worse result of Fig. 20 is probably due to the lownumber of minutiae in the ISO template, which caused awrong estimation of the orientation image in the core regionand, as a consequence, a quite different ridgeline patterngenerated. However, it should be noted that, even in thiscase, most of the original minutiae find a good match in thereconstructed image.

In spite of the high similarity of the reconstructedfingerprint patterns with respect to the original images, ata fine level of detail, several differences exist between thepatterns: this is due not only to the extra minutiae inserted inthe reconstructed images but also to some details like thelocal shape of the minutiae, the presence of evident pores,the presence of scratches or other imperfections, thestructure around the core region (which is very character-istic), and so forth.

5.2 Attacking Automatic Fingerprint RecognitionSystems

This section reports the results of experiments aimed atexploring the feasibility of a masquerade attack against eightstate-of-the-art commercial fingerprint recognition algo-rithms3 (referred to in the following as A1;A2; . . . ;A8) andagainst the algorithm available in the National Institute ofStandards and Technology (NIST) Fingerprint Image Soft-ware 2 (NFIS2) [35] (referred to in the following as NIST). Tothe best of our knowledge, all the eight commercialalgorithms (whose implementation details are industrialsecrets) use minutiae as the main feature; on the other hand, itis likely that they also exploit other features to improve theperformance [9].

For each algorithm:

. Three operating thresholds � have been selected toforce the algorithm to operate at different securitylevels, corresponding to False Match Rate ðFMRÞ ¼ 1percent, FMR ¼ 0:1 percent, and FMR ¼ 0 percent; tothis purpose, the above error rates have been a prioricomputed over the whole FVC2002 DB1 according tothe FVC2002 protocol [11].

. The 120 templates corresponding to the originalfingerprint images in the data set have been created(the internal template format of the algorithm hasbeen used, which does not necessarily correspond tothe ISO template and may contain additional data).

. The four fingerprint images, reconstructed from eachISO template with different frequency values, havebeen matched against the corresponding templatecreated by the algorithm: The attack has beenconsidered successful if at least one of the fourreconstructed images obtained a matching scorehigher than � .

. The percentage of successful attacks over the 120 fin-gerprints has been reported for the three securitylevels (FMR ¼ 1 percent, FMR ¼ 0:1 percent, andFMR ¼ 0 percent).

The attacks have been simulated under the followinghypotheses:

. BASE. Only mandatory fields are present in the ISOtemplate, and no minutiae type information isavailable ðti ¼ other for each iÞ.

. MINTYPE. Only mandatory fields are present in theISO template, but minutiae type information isavailable (ti 2 fbifurcation; terminationg for each i).

. SINGPOS. Same as BASE, but with additionalinformation about Core and Delta positions in theExtended Data.

. ORIMG. Same as BASE, but with the whole orienta-tion image stored in the Extended Data using avendor internal format; in this case, the orientationimage estimation described in Section 4.2 is notperformed, and the original orientations are used toguide the ridge pattern generation (Section 4.3).

Tables 4, 5, 6, and 7 report the results obtained under the

four above hypotheses; Fig. 21 highlights the worst/average/

best performance at each security level.The percentage of successful attacks from fingerprints

reconstructed with the proposed approach is surprising andbeyond all expectations. From the analysis of the results inthe four different hypotheses, the following observationsmay be made:

. With the sole knowledge of the minutiae position andorientation (BASE hypothesis) at a security level of


Fig. 20. The comparison of an original and a reconstructed image: Minutiae in the core region have been manually marked (circles and squares

denote matching and nonmatching minutiae, respectively).

3. The names of the commercial systems tested are not disclosed here toavoid any form of undesired publicity.


0.1 percent FMR (typical of medium-security applica-tions), the average percentage of successful attacks ishigher than 90 percent, and each algorithm testedaccepts at least 75 percent of the reconstructedfingerprints. At a much higher security level (corre-sponding to 0 percent FMR measured on FVC2002DB1), the average percentage of successful attacks ishigher than 80 percent.

. Contrary to what one may expect, knowing minutiaetype information (MINTYPE hypothesis) seems not toimprove the chance of a successful attack: in fact,although in some cases the percentage of successgrows, for most of the algorithms, it drops. This maybe explained by considering that, on one hand, thisinformation does not add any benefit against thosealgorithms (probably, the vast majority) that do not


TABLE 6Percentage of Successful Attacks under the SINGPOS Hypothesis

TABLE 7Percentage of Successful Attacks under the ORIMG Hypothesis

TABLE 5Percentage of Successful Attacks under the MINTYPE Hypothesis

TABLE 4Percentage of Successful Attacks under the BASE Hypothesis

Fig. 21. A bar graph for each security level: each graph shows the minimum, average, and maximum result obtained by the nine matching algorithms

for each of the four hypotheses.


consider minutiae type information during matching,whereas, on the other hand, the termination prototypeis more complex than the bifurcation one (see Fig. 13),and it may lead to an excessive proliferation ofminutiae during the ridgeline pattern iterativegrowth. Considering all minutiae types as “un-known” and, thus, using always the bifurcationprototype (as explained in Section 4.3) may help toreconstruct patterns that have a higher chance to beaccepted by most algorithms.

. The knowledge of the singularity positions (SING-

POS hypothesis) increases the average percentage ofsuccessful attacks only slightly: this indicates thatthe proposed optimization approach for estimatingthe orientation image is effective even when suchinformation is not available.

. If the whole fingerprint orientation image is availablein the template (ORIMG hypothesis), the probability ofa successful attack drastically increases: An average of97.78 percent has been achieved at the highest securitylevel considered. It is important to remind that, in thisexperimentation, each algorithm adopted its internaltemplate format, which may contain additional datawith respect to the ISO standard; the extremely goodresults obtained in the ORIMG hypothesis maysuggest that some of the algorithms added informa-tion about the whole orientation image or part of it totheir templates.

6 CONCLUSIONS

The most important aim of this research was to study thereconstruction of fingerprint images from standard tem-plates in order to understand whether it is possible

1. to fool a human expert (for instance placing areconstructed fingerprint image on a crime scene) and

2. to perform masquerade attacks against a state-of-the-art automatic fingerprint recognition system (forinstance, injecting a reconstructed image in a commu-nication channel or manufacturing a fake finger).

From the novel reconstruction method developed andthe results of the systematic experiments carried out, thefollowing conclusions may be drawn:

1. It is very unlikely to fool a human expert: this doesnot seem to be simply due to a limitation of theproposed approach (for example, the generation of arelatively high number of minutiae) but rather to alack of information in the standard template itself,which does not allow details such as the local shapeof the minutiae or evident pores to be reconstructed.

2. It is definitely possible to successfully attack state-of-the-art automatic recognition systems, provided thatone is able to present reconstructed images to thesystem. In the experiments performed starting fromthe sole minutiae positions and orientations (man-datory in the ISO template), the average percentageof successful attacks against nine different systemswas 81 percent at a high security level and 90 percentat a medium security level.

The latter result points out the need for new effortsaimed at making current systems

. more robust against fake fingers placed on theacquisition sensor,

. adequately protected along all the communicationchannels (for example, by encryption and challengeresponse techniques), and

. aware of the feasibility of such masquerade attacksand, hence, equipped with appropriate counter-measures. For instance, since a reconstructed imageusually contains more minutiae than the originalone, in the presence of high-quality minutiae with nomate in the template, the matching score might beconsiderably decreased.

The proposed reconstruction approach could be furtherimproved to make the success probability of a masqueradeattack even higher. Some of the possible enhancements are

. adopting more effective optimization algorithms andmodels for estimating the orientation image (whichthe experimental results are confirmed to be funda-mental for the reconstruction),

. exploiting ridge-count information (when available)to estimate the local frequency, and

. attempting to remove the spurious minutiae addedduring the ridge pattern generation.

However, the above (or other) improvements are outsidethe aims of this work, whose main purpose is not to designa perfect way to fool biometric systems, but to encouragedevelopers of algorithms and systems to seriously take intoaccount this kind of attack and to implement specificprotections and countermeasures.

ACKNOWLEDGMENTS

This work was partially supported by European Commis-

sion (BioSecure-FP6 IST-2002-507634).

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Raffaele Cappelli received the Laurea degree(cum laude) in computer science from theUniversity of Bologna, Italy, in 1998 and thePhD degree in computer science and electronicengineering from the Department of Electronics,Informatics, and Systems (DEIS), University ofBologna, in 2002. He is an associate researcherat the University of Bologna. His researchinterests include pattern recognition, imageretrieval by similarity, and biometric systems

(fingerprint classification and recognition, synthetic fingerprint genera-tion, face recognition, and performance evaluation of biometric systems).

Alessandra Lumini received the Laurea degree(cum laude) in computer science from theUniversity of Bologna, Italy, in 1996 and thePhD degree in computer science and electronicengineering from the Department of Electronics,Informatics, and Systems (DEIS), University ofBologna, in 2001, for her work on imagedatabases. She is an associate researcher atthe II Faculty of Engineering of the University ofBologna. Her research interests include pattern

recognition, biometric systems, image databases, and multidimensionaldata structures.

Dario Maio received the degree in electronicengineering from the University of Bologna in1975. He is a full professor at the University ofBologna. He is the chair of the Cesena Campusand the director of the Biometric SystemsLaboratory (Cesena, Italy). He has publishedmore than 150 papers in numerous fields,including distributed computer systems, compu-ter performance evaluation, database design,information systems, neural networks, autono-

mous agents, and biometric systems. He is the author of the booksBiometric Systems, Technology, Design and Performance Evaluation(Springer, 2005) and Handbook of Fingerprint Recognition (Springer,2003), received the PSP award from the Association of AmericanPublishers. Before joining the University of Bologna, he received afellowship from the Italian National Research Council (CNR) for workingon the Air Traffic Control Project. He is a member of the IEEE. He is withthe Department of Electronics, Informatics, and Systems (DEIS) andIEIIT-CNR. He teaches database and information systems.

Davide Maltoni is an associate professor at theDepartment of Electronics, Informatics, andSystems (DEIS), University of Bologna. Heteaches “computer architectures” and “patternrecognition” at the Department of ComputerScience, University of Bologna, Cesena, Italy.His research interests are in the area of PatternRecognition and Computer Vision. In particular,he is active in the field of Biometric Systems(fingerprint recognition, face recognition, hand

recognition, and performance evaluation of biometric systems). He is acodirector of the Biometric Systems Laboratory (Cesena, Italy), which isinternationally known for its research and publications in the field. He isthe author of the books Biometric Systems, Technology, Design andPerformance Evaluation (Springer, 2005) and Handbook of FingerprintRecognition (Springer, 2003), which received the PSP award from theAssociation of American Publishers. He is currently an associate editorof the journals Pattern Recognition and IEEE Transactions onInformation Forensic and Security. He is a member of the IEEE.

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