CIHSPS 2005 – IEEE International Conference on
Computational Intelligence for Homeland Security and Personal Safety
Orlando, FL, USA, 31 March – 1 April 2005
Spoof-Proofing Fingerprint Systems Using Evolutionary Time-Delay Neural
Networks
Reza Derakhshani
Department of Computer Science and Electrical Engineering
School of Computing and Engineering
University of Missouri, Kansas City
5100 Rockhill Road
Kansas City, Missouri 64110-2499
E-mail: reza@umkc.edu
Abstract – With the new wave of affordable, small, and easy to use
scanners, fingerprint-based biometric systems have been receiving
an increasing attention. However, a major security concern is the
possibility of intrusion by presenting a nonliving finger, be it a
duplicate or a severed finger, to an electronic fingerprint scanner in
order to gain access to a protected entity. It has been shown that
one can spoof fingerprint scanners even with play-doh® or gummy
fingers. In order to circumvent this problem, one can read signals
from the presented finger to verify its liveness and thus eliminate the
threat of synthesized or cadaver finger attacks. Earlier research has
shown that the process of perspiration on the live fingertip skin
presents a specific time progression that cannot be seen in cadaver
or synthetic fingerprint scans, and thus phenomenon can be used as
a measure of fingerprint liveness. However, the perspiration process
demonstrates itself differently on different scanning technologies
and thus a scanner-specific approach is needed. In this paper, a new
general evolutionary temporal neural network (GETnet) for
perspiration-based liveness detection is proposed. It is shown that
GETnet can arrive at a succinct solution that performs both feature
extraction and classification on the raw fingerprint ridge signals.
With the given variety of fingerprint scanners as well as the diversity
of their operating conditions, including climate and user
demographics, it is more efficient to automatically breed customized
solutions for the perspiration-based fingerprint liveness detection
through a general framework such as GETnet instead of tailoring
feature extractors and classifiers to each and every different
scenario.
Keywords – Biometrics, Liveness Detection, Time Delay Neural
Networks, Evolving Neural Networks, Evolutionary Algorithms,
Sequence Analysis, Intelligent Signal Processing, Pattern
Recognition.
I.
INTRODUCTION
Biometrics is the science of identifying or verifying
individuals’ identities based on their unique physiological
traits [1]. There has been a growing interest in biometric
applications in recent years. With the new wave of
affordable, small, and easy to use scanners, fingerprints have
been receiving more attention compared to other biometric
modalities. However, the security of a fingerprint-based
biometric system can be compromised by presenting a
nonliving finger (duplicate or a severed) that can yield an
image close enough to the one registered by the biometric
system. This is also known as a spoof attack. As long as the
spoof finger delivers the same original pattern, biometric
systems will accept the spoof as a legitimate claimant.
Different scenarios for spoof attacks continue to be
popularized by the mass media. Unfortunately, it has been
shown that this threat is real [2] and one can spoof a
fingerprint scanner even with play-doh® [3] or gummy
fingers [4], [5] with relative ease. For synthetic fingers, casts
can be formed either by direct finger impression or from
lifted latent fingerprints using photographic etching
techniques. Needless to say, the worst-case scenario would be
using the victim’s severed finger.
In order to circumvent this problem, one can read signals
from the finger in order to verify its liveness and thus
eliminate the threat of synthetic or cadaver finger attacks.
However, reading the more obvious signs of life such as
those obtained from electrocardiograms [6] and pulse
oximetry requires extra hardware which can be bulky and
expensive. Another approach is to read signs of life from the
background data that is already captured by the biometric
sensor. This method of anti-spoofing, though subtler, is more
attractive since it can be applied to the existing biometric
hardware in form of a mere software upgrade.
Our earlier research has shown that the process of
perspiration on a live fingertip skin can be seen in the
consecutive captures of an electronic scanner within the first
few seconds of the finger contact [7]. The ongoing
perspiration of a live finger presents a specific time
progression that cannot be seen in cadaver or synthetic
fingerprint scans (Fig. 1). This phenomenon has been used as
the basis for a software-based fingerprint anti-spoofing
algorithm [8] described below.
The perspiration detection algorithm uses two gray scale
fingerprints, one captured upon contact of the finger with the
scanner and another after 5 seconds. After preprocessing, the
fingerprint ridges are extracted and concatenated. These
concatenated ridges are traversed and their gray levels
recorded to obtain a ridge-signal that reflects moisture levels
of each fingerprint as the moister ridge areas are darker (Fig.
2). Features are derived from these two scans’ ridge signals.
Fig. 1. Temporal progression of perspiration as a sign of life. Left column,
top: a live finger scanned upon contact; left column, bottom: the same
fingerprint after 5 seconds. Middle column, top: play-doh® duplicate of the
same finger scanned upon contact; middle column, bottom: the same
fingerprint after 5 seconds. Right column, top: a cadaver finger scanned upon
contact; left column, bottom: the same fingerprint after 5 seconds. Note the
perspiration spots around the pores in the initial live fingerprint scan and the
specific changes in the second live fingerprint scan compared to the
relatively unchanging scans from the synthetic and cadaver fingers.
These features contain information about the dispersion of
perspiration from pores towards the drier areas of the
fingertip, which are subsequently fed to a classifier for final
decision on the liveness of the presented finger.
II. A NEW APPROACH IN CLASSIFICATION
In this section we justify the need for the suggested new
approach for feature extraction and classification of the
fingerprint ridge signals for the liveness detection task.
1. It has been shown that the perspiration based liveness
detection algorithm, which was originally developed to
circumvent spoof finger attacks on capacitive CMOS
scanners, is applicable to other fingerprint capturing
technologies such as optical and electro-optical scanners with
a varying degree of success [9]. However, the original feature
set of the perspiration based liveness detection algorithm
yields different values for different scanning technologies and
thus a scanner-specific approach might be needed [10], [11].
That is, the perspiration detection algorithm should be
customized for different capturing technologies. With the
given variety of available scanners and operating conditions,
such as climate and demographics, it is more efficient to
solve this problem through a general framework.
2. Temporal neural networks can easily handle the inputoutput format of this problem. Earlier we showed how one
can convert fingerprints to ridge signals. The target of the
liveness detection task can also be considered as a bivalued
liveness signal. This signal will assume its high value for live
fingerprint ridge signals, and will lapse into its low value
otherwise. This representation is a two-channel to one
channel sequence mapping problem, which is an ideal form
for temporal neutral networks. This approach removes the
Fig. 2. Extraction of ridge signals from a live fingerprint is demonstrated on
a magnified subsection of a fingerprint. Right: the black trace on the gray
ridges denotes the path traversed to record the gray levels which are
proportional to the ridge moisture. Left: the solid plot demonstrates the gray
level signal from the initial capture’s concatenated ridges. The dashed plot
depicts the same after 5 seconds. Horizontal axis shows the traversed ridge
pixels and the vertical axis shows their gray level.
need for features extraction from the raw ridge signals, which
is a challenging and more or less ad-hoc task since it is hard
to find a near optimal set of features for a rather vague
physiological phenomenon such as perspiration [12].
Based upon the above, a novel general evolutionary
temporal neural network (GETnet) [13], [14] is suggested for
feature extraction and classification of the fingerprint ridge
signals. GETnet is a comprehensive algorithm for evolving
general nonlinear neural networks with distributed feed
forward and recurrent delay paths. Such networks can
represent arbitrary dynamic systems [15], [16] and are at least
as powerful as Turing machines [17]. GETnet finds the
topology, size, memory depth, and synaptic connection
strengths of the sought neural network through a hybrid
system of deterministic and stochastic searches in weight,
delay, and architecture spaces. GETnet also introduces a
novel and pragmatic time-based regularization mechanism in
order to achieve minimum description length (MDL)
solutions to address the bias-variance dilemma and achieve
better generalization with smaller data sets.
III. EVOLVING THE NEURAL NET CLASSIFIER
The following summarizes GETnet’s algorithm. This
description intentionally avoids going through the details,
since the focus of this paper is the biometric application of
GETnet.
First, GETnet randomly generates a population of
temporal neural networks. Each neuron in a network is
connected either to itself or to other neurons with single or
multiple branches. Each connection branch has a specific
weight and delay. These connections can be either feed
forward or feeding back into any node. Minimum trivial
heuristics are used to ensure functionality of the networks,
such as each network and its nodes should have their input(s)
and output(s) connected to somewhere, and that zero-delay
Fig. 4. ROC curve for the 30-point test data.
Fig. 3. GETnet’s flowchart. The names of main modules are italicized, and
the output of each stage appears after a colon.
loops should be avoided. Once the functionality is checked,
each neural network is trained using scaled conjugate
gradient method [18] on the training dataset with a time that
is limited by the actual execution time of the smaller
networks in each generation. This partial, time limited
training will favor faster, more compact networks since they
can train for more epochs during the given time limit. This
race against the time results in a minimum description length
(MDL) that ensures fastest performance on the hosting
hardware (i.e. temporal MDL). Evolutionary pressure on the
network size through temporal MDL results in compact
evolved neural networks with minimum variance entailing
good generalization capabilities. The fitness of each
individual (i.e. temporal neural network) in a generation is
calculated as the inverse of its mean squared error on the
unseen validation data after partial training. The fitness score
is evaluated multiple times and averaged to include the effect
of multiple starting points within the given span of inherited
Fig. 5. Spoof, live, and cadaver concatenated training data. Top, dark grey:
first capture ridge signal; top, light grey: last capture ridge signal. Bottom:
high signal denotes the span of the ridge signal extracted from live fingers.
weights (see mutation description below for more
explanations). The better networks are given a higher chance
to parent the next generation based on their fitness values.
Parents are then mutated to form the offspring. Mutation acts
upon general strategy variables, connecting branches (delay
and weight perturbation), and the number of connections and
nodes (network sparsity and size) in form of Gaussian
perturbations. Delay perturbations adjust the distributed
memory of each network. In order to take advantage of the
partial trainings in a lineage, weights are transferred from
parent to offspring. However, to avoid local minima, noise is
added to the acquired weights (non-exact knowledge transfer
from parent to offspring). The magnitude of this noise is a
strategy variable itself and subject to evolution. This adaptive
process creates room for each new generation to explore the
performance landscape around its inherited starting point in
the weight space. It can be shown that the effect of adding
noise to weights is similar to adding noise to the target values
Fig. 6 Size of evolving networks (number of connections).
Fig. 8. Training output, best evolved network.
Fig. 7. MSE of evolving networks.
Fig. 9. Sample live test data output, best evolved network.
which can further improves generalization and convergence
[19]. Crossover is not used in GETnet since in practice it can
destroy the distributed knowledge that is spread throughout
neural networks [20]. During the deleting mutations, chained
dependencies are taken into account in order to calculate the
overall effects of deletions and avoid disruptive ones such as
removing a network’s output path, if possible. These smooth
mutations reduce the noise in evolutionary assessment of the
free parameters. The evolution cycle continues until the
required precision is reached.
After finishing the evolutionary loop, the last generation
of networks is fully trained and the best network output as
well as the average outputs of all the survivors in the last
generation are produced. The latter creates a committee of
networks that might yield a lower error in case of
independence of errors in the population. A flowchart of
GETnet is given in Fig. 3.
GETnet is more flexible and comprehensive than the
comparable temporal neural network paradigms such as
TDNN [21], FIRnet [22], Elman [23], Jordan [24], PRNN
[25], and NARMA [26]. In contrast to GETnet, all the
mentioned networks need human experts to determine their
memory and network structures as well as the other learning
parameters. This is also known as the baby-sitting problem
[27], which entails the lack of an automated mechanism to
determine the minimum required network size, an essential
issue in generalization. Furthermore, none of the above
paradigms offer an arbitrary distributed memory structure
comprised of recurrent nodes and sub-circuits as well as feedforward delay lines of variable degrees as GETnet does.
Fig. 10. Sample cadaver test data output, best evolved network.
Fig. 12. Best evolved network for the fingerprint liveness detection. Note the
novel structure with multiple feedback paths. The number beside each path
indicates the number of its parallel connections, each with its own weight
and delay.
signals (-1 for non-living and +1 for live) were used to tag the
target classes.
V. RESULTS
Below are the results generated by GETnet for our spoof
detection problem. All standard deviations refer to Gaussian
perturbations. Please see figures 4 through 12 for further
details.
Fig. 11. Sample spoof test data output, best evolved network.
IV. DATA AND SIMULATION SETTINGS
The following simulation demonstrates GETnet’s ability
to evolve appropriate compact networks for the fingerprint
liveness detection. The following settings were used:
1-Training data: 4 spoof (play-doh®), 8 live, and 4
cadaver fingerprints. Each sample contains only 150 pixels of
the corresponding fingerprint ridge signal.
2-Validation (early stopping) data: same composition as in
the training set, but each passage is only 50 pixels long.
3-Test data: 10 samples from each category (live, cadaver,
play-doh® spoof). Full-length ridge signals were used.
Given the nominal full length of 3000 to 5000 pixels for
the fingerprint ridge signals, the total size of training and
validation datasets used for training and evolution (200
pixels) constitute less than 10 percent of each sample. Bipolar
1- Evolution start, original ancestor of the best-evolved
network:
Total number of network branches = 95.
Standard deviation for perturbing the number of network
nodes = 0.0460.
Standard deviation for perturbing the number of network
interconnections = 0.0585.
Standard deviation for perturbing the connections’ delay line
depths = 0.0816.
Training and validation time (mean of multiple starts) =
314.5720 sec.
2- Evolution end, best evolved network after 15
generations:
Total number of network branches = 59.
Standard deviation for perturbing the number of network
nodes = 0.0041.
Table 1 Test outputs for live subjects. Incorrect results are italicized.
Subject
LivTst1
LivTst2
LivTst3
LivTst4
LivTst5
LivTst6
LivTst7
LivTst8
LivTst9
LivTst10
Best Net
Output
0.7596
0.8251
0.6708
0.2159
0.6020
0.7852
-0.6284
-0.2923
-0.4239
0.5883
Committee
Output
0.7701
0.8332
0.6771
0.3300
0.6617
0.7893
-0.5962
-0.2121
-0.3580
0.5769
Table 4 Confusion matrix for the test set. Threshold for output is zero.
Liveness
1
1
1
1
1
1
-1
-1
-1
1
Table 2 Test outputs for cadaver subjects. Incorrect results are italicized.
Subject
Committee
Output
-0.6383
0.7149
Liveness
CdvTst1
CdvTst2
Best Net
Output
-0.6553
0.6934
CdvTst3
CdvTst4
CdvTst5
CdvTst6
CdvTst7
CdvTst8
CdvTst9
CdvTst10
-0.1882
-0.1677
-0.7048
-0.4266
-0.6014
-0.6168
-0.6919
-0.5695
-0.0134
-0.1583
-0.6803
-0.3572
-0.5773
-0.5845
-0.6822
-0.5621
-1
-1
-1
-1
-1
-1
-1
-1
-1
1
Table 3 Test outputs for spoof subjects. Incorrect results are italicized.
Subject
SpfTst1
SpfTst2
SpfTst3
SpfTst4
SpfTst5
SpfTst6
SpfTst7
SpfTst8
SpfTst9
SpfTst10
Best Net
Output
-0.5984
-0.6311
-0.6563
-0.6493
-0.3486
-0.0479
0.0229
-0.2592
-0.4689
-0.2772
Committee
Output
-0.5494
-0.6057
-0.6423
-0.6250
-0.1599
-0.1518
0.0309
-0.3232
-0.4283
-0.2025
Liveness
-1
-1
-1
-1
-1
-1
1
-1
-1
-1
Standard deviation for perturbing the number of network
interconnections = 0.0230.
Standard deviation for perturbing connections’ delay line
depths = 0.0028.
Training and validation time (mean of multiple starts)
=151.5210 sec.
A summary of the test results is given in the tables 1
through 4. As it can be seen, 3 live claimants were falsely
recognized as nonliving, whereas one out of ten for each
spoof and cadaver test datasets was falsely recognized as live.
The overall precision is therefore (30-3-1-1)/30=83.3%. The
scalar output values in the tables were calculated as the
normalized area under each output signal yi
Output i = ∫ y i (τ ) d τ length ( y i )
(1)
The liveness results were determined as
Livenessi = hard _ threshold (Output i )
(2)
Hard limiting threshold function returns –1 for nonliving
(Outputi≤0) and 1 for living (Outputi>0) for the final
classification result. The best evolved network is depicted in
Fig. 12.
VI. DISCUSSION AND CONCLUSION
It was shown that one can evolve a general temporal
neural network and arrive at a succinct solution for the
fingerprint vitality detection problem. The demonstrated
solution not only classifies, but also performs feature
extraction by feeding the raw fingerprint ridge signals to a
recurrent time delay neural network with only four neurons.
The assigned task is the detection of a valid ongoing
perspiration pattern in order to separate live fingerprints from
the nonliving to circumvent the spoof attacks on fingerprintbased biometric systems.
During our simulation, a solution was evolved using 16
sample fingerprints. The evolving networks were exposed
only to less than %10 of each fingerprint’s ridge signal. The
resulting 4-neuron classifier and feature extractor obtained by
using this scarce amount of training data shows the ability of
GETnet’s temporal MDL to push the evolution towards a
compact and fast-acting solution that can learn from small
datasets due to its lower complexity. The compactness of the
solution can also be partially credited to the ability of GETnet
to evolve recurrent structures.
As it can be seen from Fig. 12, GETnet’s evolved solution
is nonstandard and novel compared to the existing
comparable architectures. Such novel solutions are especially
important for problems similar to the perspiration-based
liveness detection, where there might be no standard starting
point for either feature extraction or classification.
As mentioned earlier, GETnet’s novel temporal MDL
creates fast-acting networks. Such agility is considered to be
a sign of intelligence by some researchers [28]. From the
training and validation times, we see that the evolved network
is more than twice faster than its original ancestor. This is
what we desired by choosing an evolutionary pressure that
reduces network complexity using the actual execution time
on the hosting hardware for regularization. Simulations also
show that during the course of evolution the standard
deviations of mutations usually decrease [13]. This effect can
be compared to simulated-annealing [29]. The interesting
point is that this behavior emerges through evolution and is
not coded into GETnet.
In the end, it is instructive to note that the perceived
external world, i.e. the mental image of the existence as
captured by the sensory inputs of an individual, is initially
conveyed through a series of multidimensional time signals.
As human beings, we have a natural bias towards detecting
specific patterns in these streams and ignore much of the
remaining background information. Intelligent systems on the
other hand can be evolved towards a different type of sensory
acuity, and in this case perspiration detection by GETnet.
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