360 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
Fake Finger Detection by Skin Distortion Analysis
Athos Antonelli, Raffaele Cappelli, Dario Maio, and Davide Maltoni, Member, IEEE
Abstract—Attacking ﬁngerprint-based biometric systems by
presenting fake ﬁngers at the sensor could be a serious threat for
unattended applications. This work introduces a new approach
for discriminating fake ﬁngers from real ones, based on the anal-
ysis of skin distortion. The user is required to move the ﬁnger
while pressing it against the scanner surface, thus deliberately
exaggerating the skin distortion. Novel techniques for extracting,
encoding and comparing skin distortion information are formally
deﬁned and systematically evaluated over a test set of real and
fake ﬁngers. The proposed approach is privacy friendly and does
not require additional expensive hardware besides a ﬁngerprint
scanner capable of capturing and delivering frames at proper rate.
The experimental results indicate the new approach to be a very
promising technique for making ﬁngerprint recognition systems
more robust against fake-ﬁnger-based spooﬁng attempts.
Index Terms—Biometric systems, fake ﬁngers, security, skin
distortion, skin elasticity.
IOMETRIC systems offer great beneﬁts with respect to
other authentication techniques: in particular, they are
often more user friendly and can guarantee the physical pres-
ence of the user. Thanks to their good performance and to the
growing market of low-cost acquisition devices, ﬁngerprint-
based identiﬁcation/veriﬁcation systems are becoming very
popular and are being deployed in a wide range of applications:
from PC logon to electronic commerce, from ATMs to phys-
ical access control . On the other hand, it is important to
understand that, as any other authentication technique, ﬁnger-
print recognition is not totally spoof-proof. The main potential
threats for ﬁngerprint-based systems are , 
• attacking the communication channels, including replay at-
tacks on the channel between the sensor and the rest of the
• attacking speciﬁc software modules (e.g., replacing the
feature extractor or the matcher with a Trojan horse);
• attacking the database of enrolled templates;
• presenting fake ﬁngers to the sensor.
Recently, the feasibility of the last type of attack has been
reported by some researchers , : they showed that
it is actually possible to spoof some ﬁngerprint recognition
systems with well-made fake ﬁngertips (Fig. 1), created with
the collaboration of the ﬁngerprint owner or from a latent
Manuscript received January 18, 2006; revised May 3, 2006. This work was
supported by the European Commission (BioSec—FP6 IST-2002-001766). The
associate editor coordinating the review of this manuscript and approving it for
publication was Dr. Anil Jain.
A. Antonelli is with the Biometrika s.r.l., Forlì 47100, Italy (e-mail: an-
R. Cappelli, D. Maio, and D. Maltoni are with the DEIS—Università di
Bologna, Cesena (FO) 47023, Italy (e-mail: email@example.com; maio@
Digital Object Identiﬁer 10.1109/TIFS.2006.879289
ﬁngerprint; in the latter case, the procedure is more difﬁcult
but still possible.
A deep study on the feasibility of spooﬁng some commercial
ﬁngerprint scanners was performed by the authors within the
BioSec project , , . From the critical review of the
related bibliography (as described in Section II) and from the
24-months experience we accumulated by making hundreds of
fake ﬁngers with different materials and procedures and using
them to spoof existing ﬁngerprint scanners (of different types:
optical, capacitive, thermals, RF-based, etc.), we may draw
• Forging a fake ﬁnger is not as easy as some authors claim,
even when the person whose ﬁnger has to be cloned is
cooperative; it is necessary to ﬁnd the right materials to
mould the cast, learn the right process and handle with care
the artiﬁcial ﬁnger.
• Creating a fake ﬁnger from a latent ﬁngerprint is sig-
niﬁcantly more difﬁcult, requiring a skill comparable to
that of a forensic expert equipped with the appropriate
• To the best of our knowledge and from the experience
gained testing recent scanners provided with fake detec-
tion mechanisms, nowadays, in spite of the claims of some
ﬁngerprint scanner producers, no commercial ﬁngerprint
scanner (among those we tested) seems to be resistant to
well-made fake ﬁngerprints.
• The lack of satisfactory solutions to reject fake ﬁngers
shows that there are a lot of challenges in fake detection;
more research and investments on ﬁngerprint fake detec-
tion methods are needed.
This work introduces a novel method for discriminating fake
ﬁngers from real ones, based on the analysis of a peculiar char-
acteristic of the human skin: its elasticity. When a real ﬁnger
moves on a scanner surface, it produces a signiﬁcant amount of
distortion, which can be observed to be quite different from that
produced by fake ﬁngers. Usually fake ﬁngers are more rigid
than skin and the distortion is deﬁnitely lower; even if highly
elastic materials are used, it seems very difﬁcult to precisely
emulate the speciﬁc way a real ﬁnger is distorted, because the
behavior is related to the way the external skin is anchored to
the underlying derma and inﬂuenced by the position and shape
of the ﬁnger bone.
The analysis of skin distortion requires in input a sequence
of frames instead of a single static image. To this purpose,
the ﬁngerprint scanner must be able to deliver a set of frames
(Fig. 2) to the processing unit at a high speed (at least 20 frames
per second). In our study, we used the prototype of a ﬁngerprint
scanner that the company Biometrika developed within the
BioSec project  (Fig. 3).
A database of video sequences has been collected, acquiring
images both from real and fake ﬁngers. Systematic experiments
1556-6013/$20.00 © 2006 IEEE
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 361
Fig. 1. Fake ﬁngertips created with different materials. From left to right: gelatin, silicone, and latex.
Fig. 2. Set of frames acquired while a ﬁnger was rotating over the surface of a ﬁngerprint scanner.
Fig. 3. Speciﬁc version of the scanner Fx3000 (by Biometrika) that allows to acquire and transfer frames to the host at 20 fps.
have been performed to understand how much the proposed
method is capable to discriminate real from fake ﬁngers; the re-
sults achieved are very promising.
The rest of this work is organized as follows. Section II sum-
marizes the state-of-the art in this ﬁeld, Section III describes
the proposed approach, Section IV reports the experimentation
carried out to validate the new technique, and ﬁnally Section V
draws some conclusions.
Several papers have been recently devoted to this important
topic: from the analysis of potential weaknesses in generic bio-
metric systems , , , to experiments aimed at in-
vestigating how current ﬁngerprint veriﬁcation systems can be
spoofed , , , , ; from proposals of possible
solutions , , , , , , to surveys of the current
It is worth noting that the idea of spooﬁng ﬁngerprint recog-
nition systems by using a fake reproduction of the ﬁngertip is
not a novelty. The idea seems to have been described for the
ﬁrst time by the mystery writer R. A. Freeman in the book
“The Red Thumb Mark” , published in 1907. More re-
cently, James Bond in the ﬁlm Diamonds are Forever (1971)
was able to spoof a ﬁngerprint check with a thin layer of
latex glued on his ﬁngertip . However, only recently some
researchers published the results of experiments aimed at ana-
lyzing such vulnerability.
• In , the authors described two methods for creating
fake ﬁngers: duplication with cooperation and without co-
operation; in both these cases, the material used to create
362 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
the fakes was silicone; six different commercial ﬁngerprint
scanners were tested and the authors reported to be able to
spoof all of them at the ﬁrst or second attempt.
• In , it was reported that fakes created with gelatin
were more effective, in particular against scanners based
on solid-state sensors ; similar to , the authors
described cooperative and noncooperative fake-creation
methods; 11 commercial ﬁngerprint scanners were tested,
with a success rate higher than 67% for both the coopera-
tive and the noncooperative scenarios.
• In , three commercial ﬁngerprint scanners were tested:
all of them were spoofed by fake ﬁngers made of gelatin,
with a level of ease depending on the scanner and software
• In , the studies reported in  and  were extended
by testing new scanners that included speciﬁc fake detec-
tion measures; the authors concluded that such measures
were able to reject fake ﬁngers made of nonconductive ma-
terials (such as silicone), but were not able to detect con-
ductive materials such as gelatin.
The main fake ﬁnger detection techniques that have been pro-
posed to date can be roughly classiﬁed as explained in the rest
of this section.
Analysis of skin details in the acquired images: using very
high resolution sensors (e.g., 1000 dpi) allows to capture
some details that may be useful for fake detection, such as
sweat pores  or coarseness of the skin texture . In
fact, it has been experimentally noted that typical materials
used to make fake ﬁngers (e.g., gelatin) usually consist of
large organic molecules that tend to amalgamate, resulting
in a surface coarser than human skin and where small de-
tails such as pores are not present or poorly reproduced.
• Analysis of static properties of the ﬁnger: additional hard-
ware is used to capture information such as temperature
, impedance or other electric measurements , ,
odor , and spectroscopy . In , electronic noses are
used with the aim of detecting the odor of those materials
that are typically used to create fake ﬁngers (e.g., silicone
or gelatin); spectroscopy-based techniques expose the skin
to multiple wavelengths of light and analyze the reﬂected
spectrum: nonhuman tissues show a spectrum usually quite
different from human ones. Other techniques  direct light
to the ﬁnger from two or more sources and capture ﬁnger-
print images with different illuminations: the authors claim
that it is possible to discriminate between real and fake ﬁn-
gers by comparing such differently illuminated images.
• Analysis of dynamic properties of the ﬁnger, such as: skin
perspiration , , pulse oximetry , blood pulsa-
tion ,  and skin elasticity , , . To date, fake
detection by skin-perspiration is probably the technique
most deeply studied in scientiﬁc publications: the idea is
to exploit the perspiration of the skin that, starting from
the pores, diffuses in the ﬁngerprint patter following the
ridge lines, making them appear darker over time. In ,
the perspiration process is detected through a time-series
of images acquired from the scanner over a time window
of a few seconds. Skin elasticity, which produces distortion
in the acquired ﬁngerprint images , has been studied in
some previous works, but mainly focusing on the problems
that such distortion causes to ﬁngerprint matching algo-
rithms , , , , or trying to ﬁnd a mathematical
model to explain its behavior . In , it was suggested
that the acquisition of a video sequence of ﬁngerprint im-
ages could be used to deﬁne a new type of biometric fea-
ture, which combines a physiological trait (ﬁngerprint) to
behavioral traits (e.g., a particular movement of the ﬁnger
on the sensor chosen by the user); the authors underlined
that this new biometric feature, among the other advan-
tages, could be harder to be spoofed, but they did not re-
ported any experiment with fake ﬁngers. In , we brieﬂy
introduced a fake detection approach based on skin distor-
tion and reported some preliminary results. In this paper,
the whole technique is described and experiments with a
new prototype scanner are reported and discussed.
The user is required to place a ﬁnger onto the scanner surface
and to apply some pressure while rotating the ﬁnger in either
clockwise or counter-clockwise direction (this particular move-
ment has been chosen after some initial tests, as it seems quite
easy for the user and it produces the right amount of distortion).
A sequence of frames is acquired at a high frame rate during the
movement and analyzed to extract relevant features related to
skin distortion. Although the ﬁnger can be rotated at different
speed, we experimentally found that an angular speed of about
per second is optimal for measuring the distortion.
Some constraints are enforced to simplify the subsequent pro-
cessing steps; in particular
• any frame such that the amount of rotation with respect
to the previous one (inter-frame rotation) is less than
is discarded (the inter-frame rotation angle is calculated
as described in Section III-B);
is a parameter whose
optimal value has been experimentally determined as 0.25
(see Section IV-B);
• only frames acquired when the rotation of the ﬁnger is
are considered: when angle has been
reached, the acquisition halts (the rotation angle of the
ﬁnger is calculated as described in Section III-E-1).
is a parameter that was set to 15 in the experimentations
(see Section IV-B); hence, if we assume an angular speed
of about 15
per second, on the average, the user is required
to rotate the ﬁnger for about 1 s before the system informs
her or him that the acquisition process is terminated.
be a sequence of images that satisﬁes
the above constraints: each frame
, , is segmented
by isolating the ﬁngerprint area from the background; then, for
, the following steps are per-
formed (Fig. 4):
• computation of the optical ﬂow between the current frame
and the next one;
• computation of the distortion map;
• temporal integration of the distortion map;
• computation of the DistortionCode from the integrated dis-
At the beginning of the sequence, the ﬁnger is assumed re-
laxed (i.e., nondistorted), without any superﬁcial tension; this is
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 363
Fig. 4. Main steps of the feature extraction approach: a sequence of acquired ﬁngerprint images is processed to obtain a sequence of DistortionCodes.
reasonable since when the ﬁnger approaches the sensor platen
there is no skin distortion.
The isolation of the ﬁngerprint area from the background is
performed by computing the gradient of the image block-wise:
be a generic pixel in the image and a
square block of frame
centered in : each whose gra-
dient module exceeds a given threshold is associated to the fore-
ground  (Fig. 5). Only foreground blocks are considered in
the rest of the algorithm.
A. Computation of the Optical Flow
Block-wise correlation is computed to detect the new position
of each block in frame . For each block ,
denotes the estimated
Fig. 5. Fingerprint image before and after the segmentation from the back-
movement of from frame to frame . In the fol-
lowing, for simplifying the notation,
will be indicated as
364 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
Fig. 6. From left to right: two consecutive images, their difference (reported to graphically highlight the movement) and the corresponding optical ﬂow.
Fig. 7. Optical ﬂow before (on the left) and after (on the right) the regularization process.
. A graphical representation of the movement vectors (see
Fig. 6), is also known in the literature as optical ﬂow .
This method is in theory only translation-invariant but, since
the images are taken at a fast frame rate, for small blocks it is pos-
sible to assume a certain rotation- and deformation-invariance.
The block size (in pixels) is a parameter that should be
adjusted according to the sensor area and resolution. If the
blocks are too small, they do not contain enough information
to univocally identify their positions in the subsequent frame.
Otherwise, if they are too large, two problems may arise: the
algorithm would become computationally expensive and the
distortion could make the matching unfeasible. To increase
the accuracy of the optical ﬂow, the blocks can be also partially
overlapped: in this case the distance among the centers of two
consecutive blocks is smaller than the block size.
In order to ﬁlter out outliers produced by noise, by false cor-
relation matches, or by other anomalies, the optical ﬂow is then
regularized as follows.
is discarded . This step allows to remove
outliers, under the assumption that the movement of each
block cannot deviate too much from the largest movement
of the blocks of the previous frame;
is a parameter that
should correspond to the maximum expected acceleration
between two consecutive frames.
2) For each
, the value is calculated as the weighted
average of the 3
3 neighbours of , using a 3 3
Gaussian mask; elements discarded by the previous step
are not included in the average: if no valid elements are
is marked as “invalid”.
such that is discarded. This
step allows to remove elements that are not consistent with
is a parameter that controls the strength
of this procedure.
are recalculated as in step 2, but considering only the
elements retained at step 3.
Fig. 7 shows the optical ﬂow before (
vectors) and after
vectors) the steps described above.
B. Computation of the Distortion Map
The center of rotation
is estimated as the
weighted average of the positions
of all the foreground blocks
such that the corresponding movement vector is
is valid (1)
is the average of the elements in set .
The inter-frame rotation angle
(around the center ) and
the translation vector
are then computed in the
least square sense, starting from all of the average movement
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 365
Fig. 8. Graphical representation of a distortion map (A) and of the corre-
sponding integrated distortion map (B). Blocks with a lighter gray color denote
higher distortion values.
using the Gauss–Newton approach to numerically solve the
If the ﬁnger were moving solidly, then each movement vector
would be coherent with
and . Even if the movement is not
and still encode the dominant movement and, for
, the distortion can be computed as the inco-
herence of each average movement vector
with respect to
and . In particular, if a movement vector were computed
according to a solid movement, then its value would be
and, therefore, the distortion can be deﬁned as the residual
if is valid
A distortion map is deﬁned as a block-wise image whose
blocks encode the distortion values
C. Temporal Integration of the Distortion Map
The computation of the distortion map, made on just two con-
secutive frames, is affected by three problems.
• The movement vectors are discrete (because of the discrete
nature of the images) and, in case of small movement, the
loss of accuracy might be signiﬁcant.
• Errors in seeking the new position of blocks could lead to
a wrong distortion estimation.
• The measured distortion is proportional to the amount of
movement between two frames (and, therefore, depends
on the ﬁnger speed), without considering previous ten-
sion accumulated/released. This makes it difﬁcult to com-
pare a distortion map against the distortion map of another
An effective solution to the above problems is to perform a
temporal-integration of the distortion map, resulting in an in-
tegrated distortion map [Fig. 8(b)]. The temporal integration is
simply obtained by block-wise summing the current distortion
map to the distortion map “accumulated” in the previous frames.
Each integrated distortion element is deﬁned as shown in (5) at
the bottom of the page with
The rationale behind the deﬁnition is that if the norm of the
average movement vector
is smaller than the norm of the
estimated solid movement
, then the block is moving slower
than expected and this means it is accumulating tension (i.e., dis-
tortion). Otherwise, if the norm of
is larger than the norm
, the block is moving faster than expected, thus it is slip-
ping on the sensor surface releasing the tension accumulated.
The integrated distortion map solves most of the previously
listed problems: 1) discretization and local estimation errors are
no longer serious problems because the integration tends to pro-
duce smoothed values; 2) for a given movement trajectory, the
integrated distortion map is quite invariant with respect to the
ﬁnger speed. Fig. 9 shows the integrated distortion maps com-
puted for a given image sequence acquired by rotating a real
Comparing two sequences of integrated distortion maps, both
acquired under the same movement trajectory, is the basis of
this fake ﬁnger detection approach. On the other hand, directly
comparing two sequences of integrated distortion maps would
be computationally very demanding and it would be quite dif-
ﬁcult to deal with the unavoidable local changes between the
To simplify this task, a feature vector (called DistortionCode
for the analogy with the FingerCode introduced in ) is ex-
tracted from each integrated distortion map:
of increasing radius (
, , where is the radius of
the smaller annulus) are centered in
and superimposed to the
map. For each annulus
, a feature is computed as the av-
erage of the integrated distortion elements of the blocks falling
inside it (Fig. 10)
belongs to annulus (6)
The number of annuli
and the radius are parameters
that must be chosen to optimally cover a typical ﬁngerprint, ac-
cording to the sensor area and resolution.
is obtained from each frame ,
if is valid and
if is not valid
366 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
Fig. 9. Sequence of integrated distortion maps.
Fig. 10. Integrated distortion map with the annuli superimposed. Note that
background blocks are discarded at the beginning of the process (see Section III)
and therefore they are not taken into account in (6).
The DistortionCodes are invariant to rotation since distortion
values are averaged over the circular annuli; they are also in-
variant with respect to the position of the ﬁngerprint in the image
(translation), since they are centered in
; in any case, it should
be noted that translation accuracy is not critical because of the
integrated and global nature of the features adopted.
A DistortionCode sequence
is then deﬁned by normalizing
the distortion codes
The obtained DistortionCode sequence (Fig. 11) characterizes
the distortion of a particular ﬁnger under a speciﬁc movement.
Further sequences from the same ﬁnger do not necessarily lead to
the same DistortionCode sequence: the overall length might be
different, because the user could produce the same trajectory (or
a similar trajectory) faster or slower. While a minor rotation ac-
cumulates less tension, during a major rotation the ﬁnger could
slip and the tension be released in the middle of the sequence.
Therefore, a straightforward comparison of DistortionCode se-
quences is not feasible and an alignment technique like those
introduced in Sections III-E1 and III-E2 is necessary.
E. Distortion Match Function
In order to discriminate a real ﬁnger from a fake one, the
DistortionCode sequence acquired at veriﬁcation/identiﬁca-
tion time (current sequence) is compared with a reference
sequence obtained from a real ﬁnger. The reference sequence
may be a sequence acquired from the ﬁnger of the same user
during an “enrolment” session (similarly to what happens in
biometric recognition), or a predeﬁned “ideal” sequence to be
adopted for all users (in this case the fake-detection system
does not require an enrolment stage, see Section IV-C). Let
be the reference sequence and
the current sequence; a distor-
tion match function DMF
compares the reference and
the current sequence and returns a score in the range
indicating how much the current sequence is similar to the
reference sequence (1 means maximum similarity).
A Distortion Match Function must deﬁne how to do the
Step 1) Calculate the similarity between two Distortion-
Step 2) Align the elements by establishing a correspondence
between the DistortionCodes of the two sequences
Step 3) Measure the similarity between the two aligned
As to Step 1), a simple Euclidean distance between two Dis-
tortionCodes has been adopted, since it is a good metric and also
very efﬁcient to be computed, having the vectors a very small di-
mensionality. As to Step 2), two different approaches have been
• Aligning the sequences according to the accumulated inter-
frame rotation (Section III-E1).
• Aligning the sequences using dynamic time warping
(DTW)  (Section III-E2).
In both cases, the result of Step 2) is a new DistortionCode
, obtained from
during the alignment process with ; has the same car-
and the ﬁnal similarity can be simply computed
(Step 3) as the average Euclidean distance of corresponding Dis-
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 367
Fig. 11. Sequence of DistortionCodes calculated on the integrated distortion maps in Fig. 9.
Fig. 12. Example of DTW alignment. On the left, the mapping function , which maps each DistortionCode in the current sequence to a DistortionCode
in the reference sequence. On the right, a graphical representation of the same mapping: note that the same DistorsionCode in the reference sequence c
associated twice or more times (or not associated at all), not only to deal with different lengths but, more in general, to
ﬁnd the optimal alignment.
1) Aligning the Sequences According to the Accumu-
lated Inter-Frame Rotation: Any DistortionCode
be associated to the rotation angle
obtained by accumu-
lating the inter-frame rotation angles
(see Section III-B):
. This approach determines optimal pairing
between the DistortionCodes in
and according to rota-
; interpolation is used to deal with discretization
effects. The new sequence
is obtained by calculating, for
in the current sequence, a new distor-
from the two consecutive DistortionCodes in
the reference sequence
) as follows:
Equation (8) simply estimates
as the linear interpolation
of the distortion codes corresponding to the two closest rotation
2) Aligning the Sequences Using Dynamic Time Warping:
The main limitation of the previous alignment approach is that
the distortion is not only related to the amount of rotation, but
also to the pressure applied while rotating the ﬁnger, and more
in general to the movement performed, hence aligning only on
the basis of the rotation angle may be not always a good choice.
An alternative approach to align the two DistortionCode se-
quences is based on DTW . Using DTW with constrained
endpoints, slope three and the Euclidean distance as a cost func-
tion, each DistortionCode
in is associated to a Distor-
(see Fig. 12). This allows to warp
the time dimension of the reference sequence
to obtain the
The DTW algorithm aligns the two sequences according to
the less expensive path. If the two sequences are similar, the
resulting path will have a total cost low and will be quite close
to the diagonal path (Fig. 12).
3) Computation of the Final Score: Once the new sequence
has been obtained (using one
of the approaches described above), the ﬁnal score can be com-
puted as follows:
The normalization coefﬁcient
ensures that the
score is always in the range
. In fact, for any Distortion-
and for any of its ele-
, it is easy to prove that
Constraint (10) follows directly from the deﬁnition of Distor-
tionCode sequence (7), constraint (11) from the deﬁnitions of
integrated distortion map (5) and DistortionCode (6).
It is worth noting that the transformations performed to obtain
the new sequence
do not violate the two constraints in both
the proposed approaches, since:
• in the ﬁrst one,
, thus (10)
is guaranteed by the triangular inequality and (11) by the
• in the DTW approach,
, thus (10) and
(11) are trivially veriﬁed.
368 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
A. Measuring Fake Detection Errors
A ﬁngerprint scanner that embeds a fake-ﬁnger detection
mechanism has to decide, for each transaction, if the current
sample comes from a real ﬁnger or from a fake one. This deci-
sion will be unavoidably affected by errors, that should be as
low as possible: in particular, a scanner could reject real ﬁngers
and/or accept fake ﬁngers, independently of the user’s identity.
In the rest of this section, we assume a system operating in
veriﬁcation mode. Let
be the proportion of fake-ﬁnger
transactions where the system incorrectly considered the input
to come from a real ﬁnger. Let
be the proportion of
real-ﬁnger transactions where the system incorrectly considered
the input to come from a fake sample.
not be confused with identity veriﬁcation errors typical of any
biometric system; in the following, to avoid confusion we will
and the identity veriﬁcation error
rates. Under the simplifying hypothesis of no correlation be-
tween the two classes of errors (fake detection errors and iden-
tity veriﬁcation errors), and assuming the identify veriﬁcation
performance is not signiﬁcantly decreased by the fake-detection
mechanism, the overall FRR error can be estimated as
(for an au-
thorized user trying to be authenticated normally using the
real enrolled ﬁnger).
Depending on the hypotheses (Real or Fake ﬁnger, Enrolled
or Nonenrolled ﬁngerprint) under which the transaction is per-
formed, the overall FAR error can be estimated as
attacker trying to be authenticated using a real ﬁnger, dif-
ferent from the enrolled one);
(for an attacker
trying to be authenticated using a fake reproduction of a
ﬁnger which is not the enrolled one);
(for an attacker trying to
be authenticated using a fake reproduction of the enrolled
, since, even if a fake
ﬁngerprint is created by using professional equipments, its
quality is usually lower than the real ﬁnger it is designed
to imitate and therefore the chance that the identity ver-
iﬁcation algorithm does not match it with the user’s real
template is higher.
Actually, the two classes of errors (fake detection errors and
identify veriﬁcation errors) could be correlated in some cases:
for instance, a low-quality ﬁnger may determine both a high
(due, for example, to the difﬁculty of calculating the
correct optical ﬂow) and a high
(due to the few number
of minutiae that can be reliably found in its ﬁngerprint images).
It should be also considered that the adoption of a fake-detection
approach may affect the performance of the identity veriﬁca-
tion system. For instance, due to the need of measuring speciﬁc
features for fake-detection, it could be more difﬁcult to acquire
good quality images, thus increasing
(e.g., in the case of
ﬁngerprint distortion, due to the need of producing distorted im-
ages it could be more difﬁcult to acquire good quality images,
at the beginning of the image sequence, which are not affected
by distortion). Anyway, studying such correlation is beyond the
scope of this work, and will be better investigated in the future.
The experiments carried out in this study consider only fake-
detection errors (
and ), to avoid reporting per-
formance indicators depending on the identity veriﬁcation ac-
curacy of a speciﬁc biometric algorithm. There is obviously a
strict trade-off between
and : both are functions
of a fake-detection threshold
. depends also on how
much skilled the attacker is, which technologies the attacker is
able to implement, how much time and money (s)he can invest,
etc. In the experimentation performed in this work we assumed
• the attackers were experts of the application domain and
skilled in manufacturing fake ﬁngers (the fake ﬁngers man-
ufactured in our tests were made by people with 24-month
• attacks were carried out using some known methods (e.g.,
fake ﬁngers made of silicone, gelatin and other materials
• the attackers were aware of the particular fake-detection
technique adopted and did their best to defeat it (in our
tests fake ﬁngers were created trying to emulate as much
as possible the human skin deformation);
• attacks had to be performed in a short time and without live
feedback from the device.
In Sections IV-B and C,
and errors measured
in the experimentation are reported, together with the
(the value such that ).
In order to evaluate the proposed approach, a database of
image sequences was collected using a prototype ﬁngerprint
scanner by Biometrika. No public available benchmark database
could be used, due to the speciﬁc requirements of the fake de-
tection algorithm (each sample must consist of a sequence of
images acquired by a scanner while the user is rotating her/his
ﬁnger and producing distortion, and samples from both real and
fake ﬁngers acquired by the same device must be available). The
database was collected at the Biometric System Laboratory of
the University of Bologna acquiring, from each of 45 volun-
teers, two ﬁngers (thumb and foreﬁnger of the right hand); 10
image sequences were recorded for each ﬁnger. 40 fake ﬁngers
were manufactered (10 made of RTV silicone, 10 of gelatin, 10
of latex, and 10 of wood glue). Instead of making whole 3D
fake ﬁngers, we manufactured just thin layers reproducing the
ﬁngertips (see Fig. 1 for some sample pictures): this allowed
to better imitate genuine ﬁnger movements when trying to at-
tack the system. For each fake ﬁnger, 10 image sequences were
recorded. The prototype scanner produces 400
images at 569 DPI and captures images at 20 fps. In Fig. 13 and
in Fig. 14 some sample ﬁngerprint images are shown.
The volunteers received a brief training before the ﬁrst acqui-
sition. Sequences having a total ﬁnger rotation angle less than
were discarded, and the user was asked to repeat the acqui-
sition; no other quality check was adopted during the collection
of the data (for instance ensuring that a minimum amount of dis-
tortion was produced in the sequence).
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 369
Fig. 13. Sample images from the database of sequences: a real ﬁnger and four fake ﬁngers (the ﬁrst image of each sequence is shown).
Fig. 14. Some images from two sequences in the database: a real ﬁnger (top row) and a fake ﬁnger (bottom row).
ARAMETER VALUES USED IN THE
The acquisition of the image sequences from the fake ﬁngers
was performed by experts, trying to emulate as much as pos-
sible the deformation of the skin in real ﬁngers and choosing
the optimal conditions for each material; for instance, image se-
quences from fakes made of gelatin were acquired a hour after
their creation, when their elasticity is similar to the human skin,
and not later, when they become rigid and easier to be discrim-
inated from real ﬁngers.
The parameters of the approach (see Table I) were adjusted
on a totally disjoint dataset that was collected using a different
acquisition sensor (see ). The only different parameter is the
block size, which here was set to 16
16 pixels to increase the
As introduced in Section III-E, the fake detection approach
here proposed may be used in two different modalities:
• per-user reference sequence: for each user, during an en-
rolment stage, a sequence of frames is acquired from
the selected ﬁnger, the corresponding DistortionCode se-
is calculated and stored as the reference se-
quence for that user (similar to what happens with the
ﬁngerprint template to be used in a biometric recognition
• predeﬁned reference sequence: a single reference sequence
is adopted for all of the users and no enrolment stage is
required for the fake detection system.
Both of these operating modalities were experimented by
using the same test set described in the previous section.
In the per-user reference sequence modality, the following
transactions were performed on the test set:
• 4050 genuine attempts (each sequence was matched
against the remaining sequences of the same ﬁnger, ex-
cluding the symmetric matches to avoid correlation, thus
performing 45 attempts for each of the 90 real ﬁngers);
• 36 000 impostor attempts (each of the 400 fake sequences
was matched against the ﬁrst sequence of each real ﬁnger).
370 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
Fig. 15. Integrated distortion maps from the predeﬁned reference sequence used in the experimentation; it is worth noting that the shape of the deformed region
is almost elliptical and distortion is mainly conﬁned to an elliptical annulus around the center of rotation, as discussed in .
ERFORMANCE OF THE TWO ALIGNMENT APPROACHES
TWO DIFFERENT MODALITIES
Note that, since only fake-detection performance was eval-
uated (not combined with identity veriﬁcation) and con-
sidering that the proposed approach is based on the elastic
properties of real/fake ﬁngers and not on the ridge-line pat-
tern, it is not necessary that a fake ﬁnger corresponding to
the real ﬁnger is used in the impostor attempts: any fake
ﬁnger can be matched against any real ﬁnger without sig-
niﬁcantly affecting the results.
In the predeﬁned reference sequence modality, a sequence ac-
quired from a well-trained user (not included in the test data-
base) was selected as the predeﬁned sequence (Fig. 15) and the
following transactions were performed on the test set:
• 900 genuine attempts (each sequence was matched against
the reference sequence, thus performing 10 attempts for
each of the 90 real ﬁngers);
• 400 impostor attempts (each of the 400 fake sequences was
matched against the reference sequence).
Table II reports the
obtained for the two alignment
approaches (Sections III-E1 and III-E2) in the two modalities,
respectively; Fig. 16 compares the ROC graphs.
An error analysis was performed by visually inspecting the
100 real ﬁnger sequences that obtained the lowest scores in
the predeﬁned reference sequence modality with the DTW
alignment. In Table III, each sequence is labeled according to
the most evident error cause: 70% of the errors were due to an
incorrect movement (e.g., moving the ﬁnger in a nonuniform
way, translating instead of rotating,…) or a too fast movement.
Table IV analyzes the distribution of false rejection errors
among the different users; since 10 sequences were acquired
from two ﬁngers of each user, the maximum number of errors
for each user is 20. It is worth noting that all of the users were
able to provide good sequences with both the ﬁngers (only one
user had more than 10 errors among the 100 examined: 8 with
the ﬁrst ﬁnger and 4 with the second).
On a Pentium IV PC at 3.2 GHz, the feature extraction takes
about 100 ms for each frame: the most demanding step (80% of
the feature extraction time) is the correlation, whose complexity,
in the worst case, is proportional to the square of the number of
foreground pixels in the image. However, thanks to an MMX
optimization of the correlation routine, an efﬁcient implemen-
tation has been achieved. The matching step proved to be very
efﬁcient: the average time is less than 1 ms for both the align-
ment approaches. The average transaction time is about two sec-
onds, including acquisition, feature extraction, and matching.
Attacks to ﬁngerprint-based biometric systems using fake re-
productions of the ﬁnger may be a serious threat, in particular
for nonsupervised access control applications and remote au-
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 371
Fig. 16. ROC graphs of the two alignment approaches: per-user reference sequence modality (on the left) and prede
ﬁned reference sequence modality (on the
RROR ANALYSIS OF THE
100 REAL-FINGER SEQUENCES
THAT OBTAINED THE LOWEST
SCORE IN THE
MODALITY (DTW ALIGNMENT)
ISTRIBUTION OF THE
FALSE REJECTION ERRORS
AMONG THE DIFFERENT
(THE SAME SEQUENCES
ANALYZED IN TABLE
III ARE CONSIDERED)
This work introduced a fake ﬁnger detection approach based
on skin elasticity: novel techniques for extracting skin distortion
information, for encoding it as DistortionCodes, and for nor-
malizing and comparing DistortionCode sequences have been
formally deﬁned and experimentally evaluated over a test set of
real and fake ﬁngers. Two different operating modalities have
been proposed: the former (per-user reference sequence) where
the user is required to perform an “enrollment” before using
the system, the latter (predeﬁned reference sequence) where no
enrollment is required for the fake-detection (obviously enroll-
ment is still necessary for ﬁngerprint recognition).
Contrary to what one may expect, the performance of the pre-
deﬁned modality was better than that of the per-user modality.
The analysis of the main error causes for both the modalities
suggested that this behavior could probably be ascribed to these
• The reference DistortionCode sequence (which all the cur-
rent sequences were compared to) was obtained from a
well-trained user with a uniform and smooth movement,
resulting in a sequence that was able to correctly represent
most of real ﬁnger distortions and was very difﬁcult to em-
ulate using fake ﬁngers.
• During the database collection, the volunteers received
only a quick training and no speciﬁc quality control mea-
sure was enforced (except the minimum amount of ﬁnger
rotation, see Section IV-B). For this reason, a good portion
of the users did not produce enough distortion and their
corresponding DistortionCode sequences, when used as
the reference sequence in the per-user modality, were not
enough dissimilar from the fake ﬁnger sequences.
It is also worth noting that, in the per-user modality, the inter-
frame rotation angle alignment approach achieved better results
than the DTW-based one. This may be explained by considering
that, if on the one hand DTW is more ﬂexible in adapting to a
given reference sequence (potentially decreasing
the other, if no minimum quality is enforced for the reference
sequences, the greater ﬂexibility is likely to affect
We may conclude that the predeﬁned modality, besides being
simpler to be deployed in a ﬁnal system, achieves better results
with nonhabituated users; on the other hand, the performance
with the per-user modality may be increased if users are well-
trained and habituated.
We believe the experimental results are very promising; in
fact, although the system did make errors (the best
achieved was 11.24%), we must underline that what we mea-
sured in our experimentation was not the robustness with
respect to zero-effort attempts, but the robustness to attacks
carried out by experts that were aware of the speciﬁc fake-de-
tection technology and did their best to emulate the human skin
deformation. The same experts achieved a very high success
372 IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 1, NO. 3, SEPTEMBER 2006
rate (comparable to those reported in ) in spooﬁng all the
commercial devices they tested , including devices with
speciﬁc fake-ﬁnger countermeasures. However, it should be
pointed out that the proposed system, as any other fake-detec-
tion mechanisms, trades usability for security: for some large
scale low-security applications it may not be worth adopting
a fake-ﬁnger detection system, while, for other high-security
applications, the fake-detection operating threshold may be
adjusted to meet the given constraints.
Although in the experiments performed we were not able to
ﬁnd a way to make the proposed system ineffective, as for any
other similar system it cannot be totally excluded that someone
might ﬁnd a combination of techniques and materials that sig-
niﬁcantly decrease its efﬁcacy. Combining this fake-detection
system with other methods based on uncorrelated features (e.g.,
impedance, odor ) could make the resulting system even more
The proposed fake detection approach is not privacy invasive,
since it does not collect any information (such as, for instance,
blood pulsation or blood pressure) that may reveal medical dis-
eases; it has the further advantage of not requiring expensive ad-
ditional hardware, provided that the ﬁngerprint scanner is able
to acquire images at a proper frame rate.
Future work will be mainly dedicated to
• implementation and evaluation of alternative alignment
techniques for the DistortionCode sequences;
• experimentation on a larger user population;
• implementation of quality control measures for the enroll-
ment stage in the per-user modality;
• better understanding the relation between fake detection
errors and identity veriﬁcation errors.
While this paper is being written, a usability study is being
conducted by Prof. Bente’s team at the University of Cologne
where the Biometrika ﬁngerprint scanner equipped with our
fake detection approach is being experimented outside of lab-
oratory environments. The feedback from that experimentation
will help to improve the approach here introduced, thank to
the complementary information that a user-centered perspective
The authors would like to thank G. Alboni from Biometrika
(Italy) and J.-F. Mainguet from Atmel (France) for their fruitful
cooperation on the fake-ﬁnger detection topic within the scope
of the BioSec project.
 A. Antonelli, R. Cappelli, D. Maio, and D. Maltoni, “A new approach
to fake ﬁnger detection based on skin distortion,” in Proc. Int. Conf.
Biometric Authentication, Hong Kong, China, Jan. 2006.
 D. Baldisserra, A. Franco, D. Maio, and D. Maltoni, “Fake ﬁngerprint
detection by odor analysis,” in Proc. Int. Conf. Biometric Authentica-
tion, Hong Kong, China, Jan. 2006.
 A. M. Bazen and S. Gerez, “Fingerprint matching by thin-plate spline
modeling of elastic deformations,” Pattern Recognit., vol. 36, no. 8, pp.
1859–1867, Aug. 2003.
 S. S. Beauchemin and J. L. Barron, “The computation of optical ﬂow,”
ACM Comput. Surv., vol. 27, no. 3, pp. 433–467, 1995.
 BioSec European Research Project—FP6 IST-2002-001766 [Online].
 J. Blommé, “Evaluation of Biometric Security Systems Against Artiﬁ-
cial Fingers,” M.S. thesis, Linköping Univ., Linköping, Sweden, 2003.
 K. Brownlee, “Method and Apparatus for Distinguishing a Human
Finger From a Reproduction of a Fingerprint,” U.S. Patent 6 292 576,
 R. Cappelli, D. Maio, and D. Maltoni, “Modelling plastic distortion in
ﬁngerprint images,” in Proc. 2nd Int. Conf. Advances in Pattern Recog-
nition (ICAPR2001), Rio de Janeiro, Brazil, Mar. 2001, pp. 369–376.
 Y. Chen and A. Jain, “Fingerprint deformation for spoof detection,” in
Proc. Biometrics Symp., Crystal City, VA, Sep. 19–21, 2005.
 R. Derakhshani, S. A. C. Schuckers, L. A. Hornak, and L. O. Gorman,
“Determination of vitality from a non-invasive biomedical measure-
ment for use in ﬁngerprint scanners,” Pattern Recognit., vol. 36, pp.
 C. Dorai, N. K. Ratha, and R. M. Bolle, “Dynamic behavior analysis
in compressed ﬁngerprint videos,” IEEE Trans. Circuits Syst. Video
Technol., vol. 14, no. 1, pp. 58–73, Jan. 2004.
 R. A. Freeman, The Red Thumb Mark. London, U.K.: Collingwood,
 M. T. Heath, Scientiﬁc Computing: An Introductory Survey, 2nd ed.
New York: McGraw-Hill, 2002.
 A. K. Jain, S. Prabhakar, and L. Hong, “A multichannel approach to
ﬁngerprint classiﬁcation,” IEEE Trans. Pattern Anal. Machine Intell.,
vol. 21, no. 4, pp. 348–359, Apr. 1999.
 P. Kallo, I. Kiss, A. Podmaniczky, and J. Talosi, “Detector for Recog-
nizing the Living Character of a Finger in a Fingerprint Recognizing
Apparatus,” U.S. Patent 6 175 641, Jan. 16, 2001.
 H. Kang, B. Lee, H. Kim, D. Shin, and J. Kim, “A study on performance
evaluation of the liveness detection for various ﬁngerprint sensor mod-
ules,” in Proc. KES, 2003, pp. 1245–1253.
 P. D. Lapsley, J. A. Less, D. F. Pare Jr, and N. Hoffman, “Anti-Fraud
Biometric Sensor That Accurately Detects Blood Flow,” U.S. Patent
5 737 439, Apr. 7, 1998.
 D. Maltoni, D. Maio, A. K. Jain, and S. Prabhakar, Handbook of Fin-
gerprint Recognition. New York: Springer, 2003.
 T. Matsumoto, H. Matsumoto, K. Yamada, and S. Hoshino, “Impact
of artiﬁcial “Gummy”ﬁngers on ﬁngerprint systems,” Proc. SPIE, vol.
4677, Jan. 2002.
 Y. S. Moon, J. S. Chen, K. C. Chan, K. So, and K. C. Woo, “Wavelet
based ﬁngerprint liveness detection,” Electron. Lett., vol. 41, no. 20,
pp. 1112–1113, 2005.
 K. Nixon, “Novel spectroscopy-based technology for biometric and
liveness veriﬁcation,” Proc. SPIE, vol. 5404, 2004.
 Proc. Biometric Authentication ECCV Int. Workshop , ser. Lect. Notes
Comput. Sci., D. Maltoni and A. K. Jain, Eds. Prague, Czech Re-
public: Springer, May 15, 2004, vol. 3087.
 D. Osten, H. M. Carim, M. R. Arneson, and B. L. Blan, “Biometric,
Personal Authentication System,” U.S. Patent 5 719 950, Feb. 17, 1998.
 S. T. V. Parthasaradhi, R. Derakhshani, L. A. Hornak, and S. A. C.
Schuckers, “Time-series detection of perspiration as a liveness test in
ﬁngerprint devices,” IEEE Trans. Syst., Man, Cybern. C, vol. 35, no. 3,
pp. 335–343, Aug. 2005.
 T. Putte and J. Keuning, “Biometrical ﬁngerprint recognition: Don’t
get your ﬁngers burned,” in Proc. IFIP TC8/WG8.8, 4th Working Conf.
Smart Card Research and Adv. App., 2000, pp. 289–303.
 L. Rabiner and B. H. Juang, Fundamentals of Speech Recognition.
Englewood Cliffs, NJ: Prentice-Hall, 1993.
 N. K. Ratha and R. M. Bolle, “Effect of controlled acquisition on ﬁn-
gerprint matching,” in Proc. Int. Conf. Pattern Recognit., 1998, vol. 2,
 N. K. Ratha, J. H. Connell, and R. M. Bolle, “Enhancing security and
privacy in biometrics-based authentication systems,” IBM Syst. J., vol.
40, no. 3, pp. 614–634, 2001.
 ——, “An analysis of minutiae matching strength,” in Proc. 3rd Int.
Conf. Audio- and Video-Based Biometric Person Authentication, 2001,
 A. Ross, S. C. Dass, and A. K. Jain, “Fingerprint warping using ridge
curve correspondences,” IEEE Trans. Pattern Anal. Mach. Intell., vol.
28, no. 1, pp. 19–30, Jan. 2006.
 S. Schuckers, “Spooﬁng and anti-spooﬁng measures,” Inform. Security
Tech. Rep., vol. 7, no. 4, pp. 56–62, 2002.
 D. R. Setlak, “Fingerprint Sensor Having Spoof Reduction Features
and Related Methods,” U.S. Patent 5 953 441, 1999.
 L. Thalheim and J. Krissler, “Body check: Biometric access protection
devices and their programs put to the test,” C’tMag., Nov. 2002.
ANTONELLI et al.: FAKE FINGER DETECTION BY SKIN DISTORTION ANALYSIS 373
 U. Uludag and A. K. Jain, E. J. Delp, III and P. W. Wong, Eds.,
on biometric systems: A case study in ﬁngerprints,” in
Proc. SPIE, Jun.
2004, vol. 5306, Security, Steganography, and Watermarking of Multi-
media Contents VI, pp. 622–633.
 Diamonds Are Forever (Film) 1971.
Athos Antonelli received the Laurea degree in
computer science from the University of Bologna,
Bologna, Italy, in 1998.
From 1998 to 2002, he led the research team of
CT Software, Cesena, Italy, for sonar data processing
and underwater image analysis. From 2003 to 2005,
he was a Research Fellow with the Biometric System
Laboratory, University of Bologna, where he studied
new fake detection approaches within the BioSec Eu-
ropean Project. Currently, he is with Biometrika srl,
Forlì, Italy, where he leads innovative algorithms de-
velopment for biometric solutions.
Raffaele Cappelli received the Laurea degree
(Hons.) in computer science from the University of
Bologna, Bologna, Italy, in 1998, and the Ph.D. de-
gree in computer science and electronic engineering
from the University of Bologna in 2002.
Currently, he is an Associate Researcher at the
University of Bologna. He is a member of the Bio-
metric System Laboratory, University of Bologna.
His research interests include pattern recognition,
image retrieval by similarity and biometric systems
(ﬁngerprint classiﬁcation and recognition, synthetic
ﬁngerprint generation, ﬁngerprint analysis, face recognition, and performance
Dario Maio is a Full Professor at the University of
Bologna, Bologna, Italy. He is Chair of the Cesena
Campus and Director of the Biometric System
Laboratory, Cesena. He has published many papers
in numerous ﬁelds, including distributed computer
systems, computer performance evaluation, data-
base design, information systems, neural networks,
autonomous agents, and biometric systems. He is
author of the books Biometric Systems, Technology,
Design and Performance Evaluation (Springer,
2005) and The Handbook of Fingerprint Recognition
(Springer, 2003) which received the PSP award from the Association of
American Publishers. Before joining the University of Bologna, he received a
fellowship from the Italian National Research Council (C.N.R.) for working
on the air-trafﬁc-control project. He is with DEIS and IEIIT-C.N.R. where he
teaches database and information systems.
Davide Maltoni (M’05) is an Associate Professor
with the Department of Electronics, Informatics,
and Systems, University of Bologna, Bologna,
Italy. He teaches computer architectures and pattern
recognition in the Computer Science Department,
University of Bologna, Cesena. His research in-
terests are in the area of pattern recognition and
computer vision. In particular, he is active in the
ﬁeld of biometric systems (ﬁngerprint recognition,
face recognition, hand recognition, performance
evaluation of biometric systems). He is co-director of
the Biometric System Laboratory, Cesena, which is internationally known for
its research and publications in the ﬁeld. He is author of two books Biometric
Systems, Technology, Design and Performance Evaluation (Springer, 2005)
and The Handbook of Fingerprint Recognition (Springer, 2003) which received
the PSP award from the Association of American Publishers.
Dr. Maltoni is an Associate Editor of the IEEE T
FORENSICS AND SECURITY and Pattern Recognition.