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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.
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 [1], [5], [2]. 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
some conclusions.
• 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
instrumentation.
• 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 [5] (Fig. 3).
A database of video sequences has been collected, acquiring
images both from real and fake fingers. Systematic experiments
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 [18]. 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 [28], [29]
• attacking the communication channels, including replay at-
tacks on the channel between the sensor and the rest of the
system;
• 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 [19], [25]: 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-
tonelli@biometrika.it).
R. Cappelli, D. Maio, and D. Maltoni are with the DEIS—Università di
Bologna, Cesena (FO) 47023, Italy (e-mail: cappelli@csr.unibo.it; maio@
csr.unibo.it; maltoni@csr.unibo.it).
Digital Object Identifier 10.1109/TIFS.2006.879289
1556-6013/$20.00 © 2006 IEEE
I. I
NTRODUCTION
B
IOMETRIC systems offer great benefits with respect to
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.
solutions [1], [2], [9], [10], [20], [24], to surveys of the current
state-of-the-art [31].
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” [12], 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 [35]. However, only recently some
researchers published the results of experiments aimed at ana-
lyzing such vulnerability.
• In [25], 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
II. R
ELATED
W
ORKS
Several papers have been recently devoted to this important
topic: from the analysis of potential weaknesses in generic bio-
metric systems [28], [29], [34], to experiments aimed at in-
vestigating how current fingerprint verification systems can be
spoofed [6], [16], [19], [25], [33]; from proposals of possible
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 [19], it was reported that fakes created with gelatin
were more effective, in particular against scanners based
on solid-state sensors [18]; similar to [25], 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 [6], 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
characteristics.
• In [16], the studies reported in [25] and [19] 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 [18] or coarseness of the skin texture [20]. 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
[25], impedance or other electric measurements [15], [32],
odor [2], and spectroscopy [21]. In [2], 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 [7] 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 [10], [24], pulse oximetry [23], blood pulsa-
tion [17], [23] and skin elasticity [1], [11], [9]. 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 [24],
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 [18], has been studied in
some previous works, but mainly focusing on the problems
that such distortion causes to fingerprint matching algo-
rithms [3], [22], [27], [30], or trying to find a mathematical
model to explain its behavior [8]. In [11], 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 [1], 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.
III. F
AKE
F
INGER
D
ETECTION
A
PPROACH
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
15 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
less than 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.
Let be a sequence of images that satisfies
the above constraints: each frame , , is segmented
by isolating the fingerprint area from the background; then, for
each frame
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-
tortion map.
At the beginning of the sequence, the finger is assumed re-
laxed (i.e., nondistorted), without any superficial tension; this is
, the following steps are per-
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:
let 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 [18] (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
Fig. 5. Fingerprint image before and after the segmentation from the back-
ground.
in frame
. For each block
,
movement of
from frame
to frame
. In the fol-
the vector
denotes the estimated
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
[4].
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.
1) Each such that
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 consecutiv
e fra
mes.
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 i
nclu
ded in the average: if no valid elements are
present,
3) Each such that is discarded. This
step allows to remove elements that are not consistent with
their neighbours; is a parameter that controls the strength
of th
is procedure.
4) 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
bloc
ks
such that the corresponding movement vector
is
valid
is valid
(1)
where 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 sq
uare
sense, starting from all of the average movement
vectors
is marked as “invalid”.
(2)
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