ON THE ROBUSTNESS OF FINGERPRINT LIVENESS DETECTION

ALGORITHMS AGAINST NEW MATERIALS USED FOR

SPOOFING

Emanuela Marasco and Carlo Sansone

Dipartimento di Informatica e Sistemistica, University of Napoli Federico II, via Claudio 21 80125, Napoli, Italy

Keywords:

Biometrics, Liveness detection.

Abstract:

Fingerprint biometric systems may be deceived by attacks at sensor level that use fake ﬁngers. A secure

ﬁngerprint scanner is required to possess the ability to determine if the image comes from a living individual

or not. Recently, several liveness detection approaches have been proposed to address this problem. At present

performances of the existing software-based solutions have been assessed with different sensors and using

small data sets. Moreover, it is assumed that fake ﬁngerprints are produced by adopting the same materials

used for training the system. This paper looks at the cases where the test spoof ﬁnger is made by employing a

material new for the ﬁngerprint sensor. We propose an experimental comparison among the current ﬁngerprint

liveness detection approaches accomplished by adopting materials for training different than those used for

testing. Experiments have been performed by using standard databases taken from the LivDet09 Competition.

1 INTRODUCTION

Fingerprint biometric recognition systems are consid-

ered the most efﬁcient and widely adopted technique

for secure authentication. Recent research has shown

that it is not difﬁcult to deceive a ﬁngerprint recog-

nition system using fake ﬁngertips (Maltoni et al.,

2003). Common spooﬁng techniques realize fake ﬁn-

gers employing inexpensive materials, e.g., gelatin,

clay, play-doh and silicon. Then, it is required that a

ﬁngerprint sensor has the ability to determine if an in-

put biometric signal comes from a live person or not

(Schuckers, 2002). Recently, in order to address this

problem, several approaches for liveness detection

have been proposed. The state-of-the-art concern-

ing these solutions consists of two main categories:

software-based and hardware-based approaches. The

methods belonging to the ﬁrst category make use

of additional hardware to measure temperature and

heartbeat characteristics resulting in an expensive im-

plementation. The software-based approaches exploit

intrinsic vitality properties that are extracted directly

from the ﬁngerprint images acquired by the sensor

resulting in a cheaper device. At present, the main

software-based vitality detection approaches analyze

the skin perspiration through the pores, the elastic

properties of the skin and the morphology of the ﬁn-

gerprint (Coli et al., 2007) (Schuckers et al., 2006).

The main limitation of most of those methods re-

sides in the device-dependence, in fact the resolution

of the ﬁngerprint image varies across different tech-

nologies, and subsequently, the extracted characteris-

tics of vitality are not universal enough (Coli et al.,

2008). Moreover, in the previous works the systems

are tested under the assumption that fake ﬁngerprints

are realized by employing one of the materials used

for training. This assumption makes optimistic the

results showed by the authors. It is worth noting

that this aspect is a challenging problem in ﬁngerprint

liveness detection, since nowadays materials used for

fraudulent spoof attacks are going to become very so-

phisticated.

So, in order to assess the inﬂuence of this as-

pect on the performance of liveness detection algo-

rithms, in this paper we analyze the cases where spoof

ﬁngers are realized with materials that are new for

the ﬁngerprint scanner. We propose an experimen-

tal comparison among the current ﬁngerprint liveness

detection approaches accomplished by adopting ma-

terials for training different than those adopting for

testing. Experiments were carried out by using stan-

dard databases taken from Liveness Detection Com-

petition 2009 (LivDet09). The paper is organized as

follows. Section 2 presents the main steps of each

methods we have considered for our study. Section 3

describes the datasets we employed to carry out our

553

Marasco E. and Sansone C..

ON THE ROBUSTNESS OF FINGERPRINT LIVENESS DETECTION ALGORITHMS AGAINST NEW MATERIALS USED FOR SPOOFING.

DOI: 10.5220/0003270505530558

In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing (MPBS-2011), pages 553-558

ISBN: 978-989-8425-35-5

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

experiments. Section 4 reports results about the ro-

bustness of the approaches to new materials used for

spooﬁng. Section 5 draws our conclusions.

2 A TAXONOMY OF THE

EXISTING FINGERPRINT

LIVENESS DETECTION

APPROACHES

Liveness methods belong to two main categories. The

ﬁrst one exploits characteristics as the temperature of

the ﬁnger, the electrical conductivity of the skin and

the pulse oximetry. They can be detected by using

additional hardware in conjunction with the biomet-

ric sensor. This makes costly the device. The sec-

ond category performs an extra software process of

the biometric sample in order to detect the vitality

information directly from the ﬁngerprint images. In

this paper, we focus on this second category of ap-

proaches, known as software-based (Schuckers et al.,

2006). The existing software-based solutions may in-

clude dynamic or static methods.

2.1 Dynamic Approaches

Dynamic features derive from the analysis of multiple

frames of the same ﬁngers. A typical dynamic prop-

erty of a live ﬁnger is the perspiration phenomenon

that starts from the pores and evolves in time across

the ridges. This distinctive spatial moisture pattern

can be detected by observing multiple ﬁngerprint im-

ages acquired in two appropriate different times. An

interesting method based on perspiration changes in

live ﬁngers was presented by Abhyankar and Schuck-

ers in (Abhyankar and Schuckers, 2009). In this

method, the changing perspiration pattern is isolated

through a wavelet analysis of the entire ﬁngerprint im-

age. For an image processing algorithm, to quantify

the sweating pattern is challenging. Since this pattern

is a physiological phenomenon, it is variable across

subjects. Further, it presents a certain sensitivity to

the environment, the pressure of the ﬁnger, the time

interval and the initial moisture content of the skin

(Derakhshani et al., 2003). Its effectiveness requires

an efﬁcient extraction of the evolving pattern from im-

ages.

2.2 Static Approaches

Static features can be extracted from a single ﬁnger-

print impression or as difference between different

impressions. Generally, static measurements may be

altered by factors as the pressure of the ﬁnger on the

scanner surface.

According to the taxonomy proposed in (Coli

et al., 2008), features extracted by different impres-

sions can be skin deformation-based or morphology-

based, while features extracted by a single impres-

sion can be perspiration-based or morphology-based.

Elastic deformations due to the contact, the pressure

and the rotation of the ﬁngertip on the plane surface

of the sensor, are more evident in fake ﬁngerprints

made using artiﬁcial materials than in live ﬁnger-

prints. Deformation-based methods detect liveness

by comparing these distortions through static features

(Chen et al., 2005). The elastic behavior of live and

fake ﬁngers has been analyzed by extracting a speciﬁc

set of minutiae points. The second type of static fea-

tures using multiple impressions relies on a morpho-

logic investigation which exploits the thickness of the

ridges that is modiﬁed after producing the ﬁngerprint

replica.

Methods which exploit intrinsic properties of a

single impression study the skin perspiration phe-

nomenon. The vitality indication can be found by

using Wavelet Transform and Fast Fourier Transform

(Coli et al., 2007). Wavelet analysis is able to cap-

ture the non-regular shape typical of the ridges in an

image acquired from a live ﬁnger. Images taken from

artiﬁcial ﬁngers show a more regular shape. Fourier

Transform is employed to study the regular periodic-

ity of pores on the ridges in live ﬁngerprints. Such

a regularity is not present in signals corresponding to

spoof ﬁngerprints.

Liveness detection methods which search for mor-

phological characteristics of ﬁngerprint images, are

signiﬁcantly efﬁcient when based on the surface

coarseness.

3 EXISTING METHODS

EXPLOITED FOR OUR STUDY

In this section, we describe the methods employed

for our comparative analysis. Firstly, we describe the

three static morphology-based methods which exploit

a single ﬁngerprint image for vitality information ex-

traction.

Moon et al. (Moon et al., 2005) proposed a

method based on analyzing the surface coarseness in

high resolution (1000dpi)ﬁngertipimages. It has been

observed that the surface of a fake ﬁnger is much

coarser than that one of the human skin. The coarse-

ness feature is measured by computing the standard

deviation of the residual noise of the ﬁngerprint im-

age. This algorithm is fast and convenient but it

BIOSIGNALS 2011 - International Conference on Bio-inspired Systems and Signal Processing

554

works well only in presence of an high resolution

sensor (1000dpi, while the common commercial sen-

sors present a resolution of about 500dpi) (Coli et al.,

2007).

An interesting texture-based approach using a sin-

gle ﬁngerprint image was proposed by (Nikam and

Agarwal, 2009). They analyzed liveness of a ﬁn-

gerprint image by using the gray level associated to

the ﬁngerprint pixels. The gray level distribution in

a ﬁngerprint image changes when the physical struc-

ture changes. Then, real and fake ﬁngerprint images

are expected to present different textural properties.

In fact, due to the presence of sweat pores and the

perspiration phenomenon, authentic ﬁngerprints ex-

hibit non-uniformityof gray levels along ridges, while

due to the characteristics of artiﬁcial material surface,

such as gelatin or silicon, spoofﬁngers show high uni-

formity of gray levels along ridges.

In (Abhyankar and Schuckers, 2006), Abhyankar

and Schuckers proposed an approach based on multi-

resolution texture analysis and the inter-ridge fre-

quency analysis of ﬁngerprint images. They used

different texture features to quantify how the gray

level distribution in a ﬁngerprint image changes when

the physical structure changes. First order statistics

model the gray level distribution of the single pix-

els by using histograms, while second order statis-

tics refer to the joint gray level function between pair

of pixels. Two secondary features were used, Clus-

ter Shade and Cluster Prominence, based on the co-

occurrence matrix. All these features have been com-

bined with features derived from ﬁngerprint local-

ridge frequency analysis.

Secondly, we describe a static method which com-

bines characteristics describing the morphologyof the

ﬁngerprint and characteristics describing the perspira-

tion phenomenon (Marasco and Sansone, 2010). The

approach relies on static features derived from the vi-

sual texture of the ﬁngerprint image. In particular,

ﬁrst order statistics and residual noise standard de-

viation are exploited as morphology-based features,

while ratios between gray level values and individual

pore spacing are are exploited as perspiration-based

features.

The standard deviation of the residual noise mea-

sures the coarseness of the ﬁngerprint image. Ma-

terials used to make fake ﬁngers such as silicon or

gelatin consist of organic molecules which tend to ag-

glomerate, thus the surface of a fake ﬁnger is gen-

erally coarser than a live one (Moon et al., 2005).

The residual noise indicates the difference between

the original and the de-noised image, in which the

noise components are due to the coarseness of the

fake ﬁnger surface (Abhyankar and Schuckers, 2006).

In fact, according to the approach proposed by Moon

et al.(Moon et al., 2005), the surface coarseness has

been treated as a kind of gaussian white noise added

to the image.

First order statistics measure the likelihood of ob-

serving a gray value at a randomly-chosen location

in the image. The gray level associated to each pixel

is exploited to determine a vitality degree of the ﬁn-

gerprint image. They can be computed from the his-

togram of pixel intensities in the image. The goal is

to quantify the variations of the gray level distribution

when the physical structure changes. The distinction

between a fake and a live ﬁnger is based on the differ-

ence of these statistics.

Individual pore spacing characteristics are ex-

tracted after analyzing the occurrence of pores that

causes a gray value variability in the ﬁngerprint im-

age. In (Marasco and Sansone, 2010), according to

the algorithm proposed in (Derakhshani et al., 2003),

the 2-dimensional ﬁngerprint image was mapped to

1-dimensional signal which represents the gray-level

values along the ridges. The gray-level variations in

the signal correspond to variations in moisture due

to the pores and the presence of perspiration. By

transforming the signal in the Fourier domain lets to

measure this static variability in gray-level along the

ridges. In particular, the focus is on frequencies corre-

sponding to the spacial frequencies of the pores. The

FFT was computed and the total energy associated to

the spacial frequency of the pores was obtained as

static feature. The coefﬁcients of interest are from

11 to 33, since these values correspond to the spacial

frequencies (0.4 - 1.2 mm) of pores.

Intensity-based features are based on the assump-

tion that, the spoof and cadaver ﬁngerprints images

are distributed in the dark (<150), among the 256 dif-

ferent possible intensities (Tan and Schuckers, 2005).

They have computed two particular features: i) gray

level 1 ratio, corresponding to the ratio between the

number of pixels having a gray level belonging to the

range (150, 253) and the number of pixels having a

gray level belonging to the range (1, 149); ii) gray

level 2 ratio, corresponding to the ratio between the

number of pixels having a gray level belonging to the

range (246, 256) and the number of pixels having a

gray level belonging to the range (1, 245). More-

over, they have analyzed the uniformity of gray lev-

els along ridge lines and the contrast between valleys

and ridges. Real ﬁngerprints exhibit non-uniformity

of gray levels and high ridge/valley contrast values.

Then, the general variation in gray-level values of in

a spoof ﬁngerprint is less than a live one. To capture

this information the gradient of the gray-level matrix

of the image have been computed, too.

ON THE ROBUSTNESS OF FINGERPRINT LIVENESS DETECTION ALGORITHMS AGAINST NEW MATERIALS

USED FOR SPOOFING

555

Finally, we describe a perspiration-based method

using both static and dynamic features. Tan and

Schuckers (Tan and Schuckers, 2005) have experi-

mented the joint contribution of dynamic and static

features. They studied the perspiration phenomenon

from the intensity distribution perspective, by observ-

ing that live ﬁngers present a distinctive contrast be-

tween white (>250, ASCII gray level range 0:255)

and dark (<20) gray level, while spoof images have

very small contrast difference. The decision rules to

perform liveness classiﬁcation is generated after con-

sidering static and dynamic features. The static fea-

tures used in this work are based on the following pa-

rameters:

S1 =

sum(151 : 254)

sum(0 : 150)

(1)

and

S2 = sum(151 : 254) (2)

The dynamic features are based on the difference in

the histogram distribution between zero and ﬁfth sec-

ond that is larger in live ﬁnger compared to spoof

subjects. In the live ﬁngers, perspiration makes dry

(white) regions between the pores moister (darker) in

time. This approach may present some limitations in

cases of ﬁngers too dry or too moist and other perspi-

ration disorders.

4 EXPERIMENTAL RESULTS

Our experimental phase was carried out by using two

databases composed by live and spoof ﬁngerprint im-

ages. Each one refers to a different sensor (i.e.,

CrossMatch and Identix). They have been taken from

the Liveness Detection Competition 2009 (Marcialis

et al., 2009) and each one of them is composed by two

subsets, one for training and the other one for testing

the algorithm. Details about the data collection are

shown in the tables 1 and 2. In both the cases, the

subjects using for training are different with respect

to those considered for testing. Table 3 reports details

about the sensors used for LivDet 2009 Competition.

Note that the LivDet dataset has also another database

(namely, Biometrika) whose spoof ﬁngerprints have

been produced by employing only one material (Sili-

con). So, for carrying out the current comparison, we

have adopted only CrossMatch and Identix databases

in which spoof ﬁngerprints have been made by em-

ploying three different materials.

The classiﬁcation performance evaluation was

performed by adopting the same parameters used dur-

ing the LivDet09, deﬁned as follows:

• Ferrlive: rate of misclassiﬁed live ﬁngerprints.

Table 1: Datasets of images used for training.

Database Sub Live Fake Frames

Identix 35 375 375 0 and 2 sec

CrossMatch 63 500 500 0 and 2 sec

Table 2: Datasets of images used for testing.

Database Sub Live Fake Frames

Identix 125 1125 1125 0 and 2 sec

CrossMatch 191 1500 1500 0 and 2 sec

Table 3: Fingerprint sensors used for LivDet 2009.

Scanners Model No. (dpi) Size

Biometrika FX2000 569 (312x372)

Identix DFR2100 686 (720x720)

CrossMatch Veriﬁer 300 LC 500 (480x640)

• Ferrfake: rate of misclassiﬁed fake ﬁngerprints.

In particular, performance is measured by using the

value e averaged on the two database CrossMatch and

Identix. The value e is computed as follows:

e =

Ferrlive + Ferr f ake

(3)

In our ﬁrst experiments, each system was trained

by using features extracted from fake samples made

with all the materials available in each database. In

particular, in both Identix and CrossMatch databases,

the materials employed are Gelatin, Silicon and Play-

Doh. Then, we have carried out a further evalua-

tion, in order to study the robustness of the exist-

ing liveness detection approaches with respect to un-

known materials used for producing fake ﬁngers. In

this experiment, each system was trained by using

spoof ﬁngerprints realized with all but one of the

available materials, while the excluded material was

used for testing. Table 4 reports the performance of

the method proposed by Marasco and Sansone. In

presence of high resolution images, taken from the

Identix database, the testing performed using Gelatin

and Silicon, when the training is performed by em-

ploying fake ﬁngers made in Play-Doh, gives rise

to a good spooﬁng recognition rate. Table 5 shows

that the method proposed by Moon et al. wrongly

classiﬁes the majority of the fake ﬁngerprints taken

from CrossMatch database, while for a higher res-

olution factor, such a method presents a better be-

havior in presence of unknown materials using for

spooﬁng. Table 6 and 7 show that the variation in

fake materials does not signiﬁcantly affect the per-

formance of both Nikam-Agarwal and Abhyankar-

Schuckers approaches, when the training set is only

composed by samples made with Gelatin. On the con-

trary, as reported in Table 8, the performance of the

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Table 4: Performance of the method proposed by Marasco and Sansone on CrossMatch and Identix databases.

CrossMatch Identix

Gelatin Play − Doh Silicon Gelatin Play− Doh Silicon

Ferrlive 6.5% 5.7% 12.6% 3.8% 19.2% 9.7%

Ferr fake 25.9% 16.7% 10.0% 42.3% 5.5% 30.6%

e 16.2% 11.2% 11.3% 23.05% 12.35% 20.15%

Table 5: Performance of the method proposed by Moon et al. on CrossMatch and Identix databases.

CrossMatch Identix

Gelatin Play − Doh Silicon Gelatin Play − Doh Silicon

Ferrlive 12.30% 15.00% 35.70% 45.20% 79.60% 40.80%

Ferr fake 63.10% 61.80% 47.30% 31.80% 4.20% 36.80%

e 37.70% 38.40% 41.50% 38.50% 41.90% 38.80%

Table 6: Performance of the method proposed by Nikam and Agarwal on CrossMatch and Identix databases.

CrossMatch Identix

Gelatin Play − Doh Silicon Gelatin Play − Doh Silicon

Ferrlive 27.20% 43.70% 24.20% 23.50% 29.30% 20.00%

Ferr fake 22.00% 32.90% 31.60% 16.00% 28.80% 31.50%

e 24.60% 38.30% 27.90% 19.75% 29.05% 25.75%

Table 7: Performance of the method proposed by Abhyankar and Schuckers on CrossMatch and Identix databases.

CrossMatch Identix

Gelatin Play − Doh Silicon Gelatin Play − Doh Silicon

Ferrlive 45.80% 29.80% 58.60% 65.50% 61.60% 37.90%

Ferr fake 12.20% 24.40% 17.00% 2.40% 46.40% 27.70%

e 29.00% 27.10% 37.80% 33.45% 54.00% 32.80%

Table 8: Performance of the method proposed by Tan and Schuckers on CrossMatch and Identix databases.

CrossMatch Identix

Gelatin Play − Doh Silicon Gelatin Play − Doh Silicon

Ferrlive 38.60% 24.40% 54.80% 64.10% 36.00% 38.80%

Ferr fake 32.20% 39.20% 43.00% 28.70% 42.40% 13.20%

e 35.40% 31.80% 48.90% 46.40% 39.20% 26.00%

Table 9: Performance of the analyzed approaches in terms of the average error e on Identix and CrossMatch databases.

Marasco-Sansone Moon et al. Nikam-Agarwal Abhyankar-Schuckers Tan-Schuckers

Gelatin 19.63% 38.10% 22.18% 31.23% 40.90%

Play-Doh 11.78% 40.15% 33.68% 40.55% 35.50%

Silicon 15.73% 40.15% 26.83% 35.30% 37.45%

Avg 15.71% 39.47% 27.53% 35.79% 37.45%

All materials 12.45% 30.85% 24.53% 39.37% 29.20%

Tan-Schuckers method seems quite dependent on the

material as well as on the considered dataset.

As resumed in Table 9, when the material used to

attack the system is not known during the training,

most of the algorithms decrease in performance. This

conﬁrms our claim that the performance of liveness

detection algorithms reported by the authors typically

represents an overestimate of that obtainable in real

scenarios. Among the considered methods, the one

based on a single feature (Moon et al., 2005) is the

most dependent on the use of unknown materials for

testing. Also the dynamic method proposed in (Tan

ON THE ROBUSTNESS OF FINGERPRINT LIVENESS DETECTION ALGORITHMS AGAINST NEW MATERIALS

USED FOR SPOOFING

557

and Schuckers, 2005) had a signiﬁcant decrement in

performance when classifying fake ﬁngerprints real-

ized with materials different from those present in the

training set. The other methods are instead more ro-

bust, and the one proposed by Marasco and Sansone,

which is based on a combination of multiple features,

exhibited the best average error e when the material

used for testing is unknown at training time.

5 CONCLUSIONS

In this paper, we have analyzed the impact of a new

material on the performance of the existing liveness

detection algorithms. This analysis has been per-

formed by considering three different sensors.

Our experiments showed that the performance of

liveness detection approaches in which only few fea-

tures are exploited, signiﬁcantly decreases in pres-

ence of spoof attacks realized by employing materi-

als different from those using for training the system.

This weakness can be reduced by combining multiple

vitality features. In particular, the more robust ap-

proach was given by the joint usage of morphology-

and perspiration-based features.

ACKNOWLEDGEMENTS

The authors would like to thank Prof. Stephanie

Schuckers of the Clarkson University (USA) for her

useful talks, Prof. Luisa Verdoliva of the University

of Naples (Italy), for supporting the wavelet analy-

sis techniques and Prof. Davide Maltoni of the Uni-

versity of Bologna (Italy), for his valuable comments

about the existing core detection algorithms.

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