Handwritten Signature Verification for Mobile Phones
Nilakantha Paudel
1
, Marco Querini
2
and Giuseppe F. Italiano
2
1
Roma Tre University, Department of Mathematics, Largo San Leonardo Murialdo, 1, 00146, Roma, Italy
2
University of Rome ”Tor Vergata” via del Politecnico 1, 00133, Roma, Italy
Keywords:
Handwritten Signature, Signature Verification, Mobile Signature, Identity Verification, Biometric Security.
Abstract:
Handwritten Signature Verification (HSV) systems have been introduced to automatically verify the authen-
ticity of a user signature. In offline systems, the handwritten signature (represented as an image) is taken
from a scanned document, while in online systems, pen tablets are used to register signature dynamics (e.g.,
its position, pressure and velocity). The main contribution of this work is a new HSV algorithm specifically
designed for running on low-end mobile devices. Towards this end, we report the results of an experimental
evaluation of our system on different handwritten signature datasets.
1 INTRODUCTION
Biometrics examine the physical or behavioral traits
that can be used to determine a person’s identity. Bio-
metric recognition is the automatic recognition of a
person based on one or more of these traits. This
method of authentication ensures that the person is
physically present at the point-of-identification and
makes unnecessary to remember a password or to
carry a token. The most popular biometric traits used
for authentication are face, voice, fingerprint, iris and
handwritten signature.
In this paper, we focus on handwritten signature
verification (HSV), which is a natural and trusted
method for user identity verification. HSV can be
classified into two main classes, based on the device
used and on the method used to acquire data related
to the signature: online and offline signature verifica-
tion. Offline methods process handwritten signatures
taken from scanned documents, which are, therefore,
represented as images. This means that offline HSV
systems only process the 2D spatial representation of
the handwritten signature (i.e., its shape). Conversely,
online systems use specific hardware, such as pen
tablets, to register pen movements during the act of
signing. For this reason, online HSV systems are able
to process dynamic features of signatures, such as the
time series of the pen’s position and pressure.
Online HSV has been shown to achieve higher ac-
curacy than offline HSV (Qiao et al., 2007; Kalera
et al., 2004; Jain et al., 2002), but unfortunately it
suffers from several limitations.
In fact, handwritten signatures are usually ac-
quired by means of digitizing tablets connected to a
computer, because common low-end mobile devices
(such as mobile phones) may not be able to support
the verification algorithms (due to their hardware con-
figuration capacity to compute the algorithm) or may
be too slow to run the verification algorithm (due
to limited computational power). As a result, the
range of possible usages of the verification process
is strongly limited by the hardware needed. To over-
come this limitation, one needs techniques capable of
verifying handwritten signatures acquired by smart-
phones and tablets in mobile scenarios with very high
accuracy.
Online HSV systems (such as (Xyzmo, 2013; Su-
tiDSignature, 2013; Andxor Corporation, 2013; Tre-
vathan and McCabe, 2005; Mailah and Lim, 2012))
are able to address only partially these issues: they are
supported by mobile devices, but they are not inher-
ently designed for common low-end mobile devices
such as mobile phones; several approaches make use
of pen pads (special purpose hardware for handwrit-
ing), signature tablet (special purpose desktop and
mobile hardware for signing), interactive pen displays
(complete instruments for working in digital applica-
tions), Kiosk systems and PC Tablets.
As for the online HSV systems described in (Krish
et al., 2013; Mendaza-Ormaza et al., 2011; Blanco-
Gonzalo et al., 2013), even if experiments related to
online HSV were carried out on low-end devices in
46
Paudel, N., Querini, M. and Italiano, G.
Handwritten Signature Verification for Mobile Phones.
DOI: 10.5220/0005675200460052
In Proceedings of the 2nd International Conference on Information Systems Security and Privacy (ICISSP 2016), pages 46-52
ISBN: 978-989-758-167-0
Copyright
c
 2016 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved

order to evaluate the verification accuracy, no analysis
addressing the computational time is used in the algo-
rithm design (which is particularly important, due to
the limited computational power of mobile devices).
The goal of this paper is to address the above chal-
lenges by designing a new method which can be run
on low-end devices too. The novelties of our approach
lie mainly in the following aspects.
First, we propose a method for the verification
of signature dynamics which is compatible to a wide
range of low-end mobile devices (in terms of compu-
tational overhead and verification accuracy) so that no
special hardware is needed.
Secondly, our new method makes use of several
technical features that, to the best of our knowledge,
have not been previously used for handwritten signa-
ture recognition.
Finally, in order to assess the verification accuracy
of our HSV system, along with the average computa-
tional time, we conduct an experimental study whose
results are reported for different data sets of signa-
tures.
2 FEATURES OF THE ONLINE
SIGNATURES
2.1 Dynamics
An online handwritten signature on a digital device
is a series of points, and each point is represented
by a vector in four dimensions, X, Y, Pressure and
Time. We define these series of points as dynamics
of the signature. When the user writes the signature,
s/he might do pen-up and pen-down moves rather than
moving the pen tip continuously. We define a stroke
(ST) as the trajectory of a pen tip between a pen-down
and a pen-up. A signature can be can be partitioned
into multiple strokes as shown in Figure 1 and in Fig-
ure 2.
Figure 1: Handwritten online signature.
• X,Y: The x and y coordinates of each sampled
point which is captured from the device screen.
Figure 2: Strokes of the signature shown in Figure- 1,
blocks are in left to right and top to bottom order.
Since the user may put his/her signature on any re-
gion of the screen, the X-Y coordinates are always
translated so that the point given by the mean co-
ordinates becomes the new origin of the coordi-
nate system.
• Pressure (P): The pressure with which the screen
is pressed. When the pen is down, or when the
user draws the line continuously, then the pres-
sure value becomes 1 (maximum value) for that
points. Similarly, when the pen is released from
the screen, then pressure value becomes 0 (mini-
mum value) for that specific point.
• Time Series (TS): The sequence of equispaced
sampling time instants. The sampling period, i.e.,
the time difference between two consecutive sam-
ples, is constant and exactly equal to the inverse
of the device sampling frequency.
2.2 Features
We use the features to study the structure of the sig-
nature, and its strokes from the different perspective.
Each features are equally important for the registra-
tion and verification steps. The only things is they
will verify one after another. Why they are important
and how they do the work for the signature registra-
tion and verification steps, we will explain in section
3.1 and 3.2. The statistical and mathematical tools
over the dynamics are used to calculate the features
as follows.
2.2.1 Features of the Signature
(i) Pen-Up number: Total number of pen-ups done
by the user while s/he write his/her full signa-
ture.
(ii) Path length(PL): The path length is the total path
length travel by the user pen tip during the sig-
nature creation. The device sampling frequency
gives the value of each dynamics in equal inter-
val of time and the euclidean distance formula
Handwritten Signature Verification for Mobile Phones
47

calculate the distance between them in each in-
terval as follows.
PL =
n
∑
i=2
p
(x
i
−x
i−1
)
2
+ (y
i
−y
i−1
)
2
where x
i
∈
X and y
i
∈ Y
(iii) Diagonal length(DL): We extract the
maximum (x
max
,y
max
) and minimum
(x
min
,y
min
) points in X,Y. Then we use the
euclidean distance formula for 2D, DL=
(
p
(x
max
−x
min
)
2
+ (y
max
−y
min
)
2
).
(iv) Time Length(TL): The total time in milliseconds,
which has taken by the user to write his com-
plete signature (the time duration between the
first pen down and last pen up).
(v) Mean Speed(MS): The average of user writing
speed for the signature. We have four different
dynamics sets (X, Y, TS, P) of equal size. All
points of these sets are sequential and tracked on
the equal time interval from the device’s screen
with frequency. We calculate the speed/velocity
between two subsequent points and sum them.
At the end, we divide the total sum by the total
number of points.
MS =
1
n
n
∑
i=2
√
(x
i
−x
i−1
)
2
+(y
i
−y
i−1
)
2
(t
i
−t
i−1
)
, where x
i
∈ X ,
y
i
∈ Y and t
i
∈ TS.
(vi) Covariance-XY(CXY): In order to measure
the scattered of the points on the signa-
ture path we calculate the CXY as follows:
CXY =
1
n
n
∑
i=1
p
(x
i
)
2
+ (y
i
)
2
, where, x
i
∈ X, y
i
∈
Y. We already translated X,Y with mean origin.
So, we don’t have to calculate the mean again.
(vii) Vector length ratio (VLR): Each point of the sig-
nature captured by the acquiring device has a 4
dimensional representation (X, Y, TS, P). As for
VLR, we only focus on x-axis and y-axis. We
calculate the sum of the length of all the vectors
drawn from the origin to each point (X,Y) coor-
dinates. Finally, the sum is divided by PL.
VLR =
1
PL
n
∑
i=1
p
(x
i
−x
origin
)
2
+ (y
i
−y
origin
)
2
,
where, x
i
∈ X, y
i
∈ Y.
2.2.2 Features of the Strokes
Stroke is the subsequence of a signature sequence. It
has exactly the same features and dynamics that sig-
nature has.So, we calculate the following proportion
of dynamics for each stroke over the full signature.
(i) Path Length Ratio (PLR):
PL of the stroke
PL of the signature
(ii) Time Length Ratio (TLR):
TL of the stroke
TL of the signature
(iii) Diagonal Length Ratio (DLR):
DL of the stroke
DL of the signature
(iv) Mean Speed Ratio (MSR):
MS of the stroke
MS of the signature
(v) Covariance XY Ratio (CXYR):
CXY of the stroke
CXY of the signature
(vi) Stroke Vector Length Ratio (STVLR):
VLR of the stroke
VLR of the signature
3 THE SIGNATURE
VERIFICATION ALGORITHM
We describe next the registration and verification pro-
cess from a technical perspective.
3.1 Signature Registration Phase
In this phase, the system takes the user’s genuine sig-
natures as input and generates the biometric template
of the features with the following steps.
3.1.1 Acquisition and Pre-processing
In the acquisition phase, the user has to write the sig-
natures with the same number of pen ups for three
rounds as input. In each round, whenever the signa-
ture is captured from the screen, the pre-processing
starts immediately. Then, the system eliminates the
noise, normalizes the path and all kind of features are
calculated and then checked with the features of exist-
ing signatures. In the checking process, the signature
should have exactly the same number of pen ups.
The area covered by the signature and its length
depend on the screen sizes. Since various devices may
have different screen sizes and the feature values (PL,
DL, TL, MS, CXY, VLR, PLR, DLR, TLR, MSR, CXYR,
STVLR) depend on the screen pixel density, we use a
certain tolerance factor to register the signature. For
this reason, each feature should be similar with the
features of existing signatures and their correspond-
ing strokes by a certain tolerance factor. Otherwise,
the user has to write the signature again for that round.
3.1.2 Template Generation and Store
Once pre-processing is completed, then the system
has the features and dynamics of all the three sig-
natures. So in this step, we calculate the average of
each features as follows:
1
3
3
∑
i=1
Feature
(i)
and create
ICISSP 2016 - 2nd International Conference on Information Systems Security and Privacy
48

an interval for each feature with its average value by
certain threshold factor.
We also used the dynamic time warping (DTW)
for template generation and signature verification pro-
cess. In time series analysis, dynamic time warping
(M
¨
uller, 2007) is an algorithm for measuring sim-
ilarity between two temporal sequences which may
vary in time or speed. In addition it has also been
used for partial shape matching application. More-
over, it has been used in literature for both on-line
and off-line HSV successfully (Faundez-Zanuy, 2007;
Miguel-Hurtado et al., 2007; Piyush Shanker and Ra-
jagopalan, 2007). There are different kind of algo-
rithms to check the similarity between the sequences
like Fr
`
echet distance but we use DTW. This is because
of its high accuracy and efficiency (in terms of com-
putational time) which is well suited for our algorithm
that is specially designed for mobile devices.
3.2 Signature Verification Phase
In the verification phase, the system makes the de-
cision on whether the claimed signature is genuine
or forged. We already calculated the accepted inter-
val for each features in the template generation phase.
The steps for the verification process are as follows:
3.2.1 Check with Global Features of the
Signature
(i) Check with Pen up Number: If the claimed sig-
nature has a different number of pen-ups, then it
will be rejected.
(ii) Check with all features of the signature (PL, DL,
TL, MS, CXY, VLR) respectively:
If each feature of the claimed signature does not
fall in its corresponding interval generated by
template then it will be rejected.
3.2.2 Check with Features of the Strokes
The claimed signature may have more than a single
stroke. For every stroke, the system checks all the fea-
tures (TLR, DLR, MSR, CXYR). Each feature should
lie in the corresponding interval that was generated
at the template generation phase. The system counts
how many strokes pass the test. If this percentage is
lower than a certain threshold then the signature is re-
jected.
3.2.3 Check with DTW
If the claimed signature passes all the above verifica-
tion steps, then DTW is applied on it as follows.
Let m be the total number of strokes in a sin-
gle signature. Then by using the feature of each
signature, the following m-dimensional vector is
computed. Let the i
th
stroke (related to feature f of
signature) of the j
th
signature in a 1D time series be
denoted as S
i
j
. DTW (S
i
j
,S
i
k
) denotes the 1D DTW
method applied to the i
th
segments of the j
th
and k
th
signatures.








f
1
f
2
...
f
m








=









DTW (S
1
1
,S
1
2
)+DTW (S
1
1
,S
1
3
)+DTW (S
1
2
,S
1
3
)
3
DTW (S
2
1
,S
2
2
)+DTW (S
2
1
,S
2
3
)+DTW (S
2
2
,S
2
3
)
3
...
DTW (S
m
1
,S
m
2
)+DTW (S
m
1
,S
m
3
)+DTW (S
m
2
,S
m
3
)
3









When


f


vector is computed for each feature f , we
get a


X


vector (x coordinates), a


Y


vector (y co-
ordinates), a


P


vector (P coordinates), and a


T


vector (T S coordinates).
Finally, we combine the metrics with the follow-
ing sums,








d
1
d
2
...
d
m








=








X
1
+Y
1
+ P
1
+ ...+ T
1
X
2
+Y
2
+ P
2
+ ...+ T
2
...
X
m
+Y
m
+ P
m
+ ...+ T
m








The output distance vector


d


represents the “dis-
tance” among the three signatures. The whole pro-
cess is repeated twice; the first time between the
genuine registered signatures (


d
g


as output, which
is already calculated during the template generation
phase) and the second time between the claimed sig-
nature and registered signatures (


d
v


as output). In
the template generation phase, we also calculated the
interval by using the threshold factor in


d
g


. So if


d
v


does not lie in that interval, then the claimed
signature is rejected, otherwise accepted.
Now, we turn to describe our algorithm. First, the
total pen up number is considered. If the signature
to be verified has a different number of pen-ups, then
the signature is assumed to be a forgery. If the forger
writes the signature very fast then he/she produces
the better line quality with less accuracy. Similarly,
if he/she writes very slowly then the signature may
be more accurate but the line quality is poor, and the
time length is unnaturally high. So in either case, our
algorithm works because of TL.
During the template generation phase, the user is
totally free to write the genuine signature on the de-
vice screen. So, we calculate the features and DL for
his/her signature from device perspective. Now, if the
forger writes the signature on all the available area
then it has a very high features and DL. Similarly, if
he writes in a small area then it will have very low
Handwritten Signature Verification for Mobile Phones
49

features and DL. In either case the algorithm works to
reject it.
Even if the forger writes the signature within a
given area with expected length and time, it is really
difficult to write the signature with tolerable MS for
the forger, even for the real user, s/he cannot write the
signature with same MS as before, but s/he can write
his/her signature within the tolerable interval of MS.
Whereas forger can’t do it and our algorithm can eas-
ily recognize his speed and reject it.
CXY measures the scatter value of all points in
a signature that are distributed on the device screen.
So, even if the forger writes a signature matching
PL, TL, MS, it is unlikely to match the distribution
of the points with the genuine signatures. So, when-
ever his/her signature does not match with CXY then
our algorithm detects that it is a forgery.
A signature may have multiple strokes and each
stroke has its own features (PL, TL, DL, MS, CXY and
STVLR ) because it is just a subsequence of the sig-
nature sequence. The features of each stroke are dif-
ferent from each other. So, our algorithm calculates
all the features of each stroke and then finds out its
ratio to the whole signature. So, even if the forger is
able to write a signature which successfully passes all
the global features test, still, if it does not passes the
stroke ratio verification process then the signature is
rejected.
Finally, if the forgery passes all the global and
stroke feature tests, then, the signature undergoes
DTW testing. DTW compares the similarity between
two sequences. We find out two distance vectors:


d
g


represents the “distance” among the three gen-
uine signatures, while


d
v


represents the “distance”
among three genuine with claimed signature. If


d
v


does not lie in the interval which is calculated on the
basis of


d
g


by certain threshold at the template gen-
eration phase, then it is rejected as a forgery.
4 EXPERIMENTATION
In this section we present experimental results con-
cerning identity verification with our system. The ac-
curacy of a recognition algorithm is generally mea-
sured in terms of two potential types of errors: false
negatives (fn) and false positives (fp). fp are cases
where a claimed identity is accepted, but it should not
be, while fn are cases where a claimed identity is not
accepted, while it should be. The frequency at which
false acceptance errors occur is denoted as False Ac-
ceptance Rate (FAR), while the frequency at which
false rejection errors occur is denoted as False Rejec-
tion Rate (FRR). Two metrics building on true/false
positives/negatives (tp,fp,tn,fn) are widely adopted:
precision and recall. Recall (tp/(t p+ f n)) is the prob-
ability that a valid identity is accepted by the system
(i.e., true positive rate) while precision (tp/(t p+ f p))
is the probability that a claimed identity which is ac-
cepted by the system is valid. F-measure (which is
the harmonic mean of precision and recall) combines
both metrics into a global measure ( f -measure = (2×
prec ×recall)/(prec + recall)).
A threshold on the similarity score must be identi-
fied for determining whether two signatures are sim-
ilar (accept the identity) or significantly different (re-
ject the identity). The higher the threshold, the higher
the precision (i.e., the lower the risk of accepting in-
valid identities). However, a high threshold also de-
creases the recall of the system (i.e., the higher the
risk to reject valid identities).
The performance of the proposed scheme has
been assessed in terms of false positives, false neg-
atives, precision, recall and f-measure on three differ-
ent datasets: on the SigComp2011 Dutch and Chinese
datasets (Liwicki et al., 2011); on the SigComp2013
Japanese dataset (Malik et al., 2013).
We start by describing the experimental set-up.
Several mobile devices have been involved in our ex-
periments (i.e., Google Nexus 5, GalaxyS2, XperiaZ2
and ZTE Blade A430), along with several standard
datasets. The specification of the datasets involved
are as follows:
• The SigComp2011 (Liwicki et al., 2011) compe-
tition involved (online) dutch and chinese data.
The purpose of using these two data sets was to
evaluate the validity of the participating systems
on both Western and Chinese signatures. Signa-
ture data were acquired using a WACOM Intuos3
A3 Wide USB Pen Tablet and collection software,
i.e., MovAlyzer.
– Dutch Dataset. The dataset is divided in
two non-overlapping parts, a training set (com-
prised of 10 authors with 330 genuine signa-
tures and 119 forgeries) and a test set (com-
prised of 10 authors with 648 genuine signa-
tures and 611 corresponding forgeries).
– Chinese Dataset. The dataset is divided in
two non-overlapping parts, a training set (com-
prised of 10 authors with 230 genuine signa-
tures and 430 forgeries) and a test set (com-
prised of 10 authors with 120 genuine signa-
tures and 461 corresponding forgeries).
• The SigComp2013 (Malik et al., 2013) compe-
tition involved (online) data collected by PRre-
searchers at the Human Interface Laboratory, Mie
University Japan.
ICISSP 2016 - 2nd International Conference on Information Systems Security and Privacy
50

– Japanese Dataset. The signature data were
acquired using a HP EliteBook 2730p tablet
PC and self-made collection software built with
Microsoft INK SDK. The whole dataset con-
sists of 1260 genuine signatures (42 speci-
mens/individual) and 1080 skilled forgeries (36
specimens/forgery). The dataset is divided in
two non-overlapping parts, a training set (com-
prised of 11 authors with 42 genuine signa-
tures of each author and 36 forgeries per author)
and a test set (comprised of 20 authors with 42
genuine signatures each and 36 corresponding
forgeries per author).
The experimental results in terms of precision, re-
call and f-measure (that vary according to the chosen
thresholds) have been used for tuning the thresholds
in order to get better performance.
The remainder of this section illustrates our
results.
Table 1: Precision(PCR), recall(RCL), f-measure(FMR),
FAR and FRR as functions of a tolerance factor(TF).
TF PCR RCL FMR FRR FAR
34% 0.983 0.919 0.943 0.008 0.008
35% 0.969 0.934 0.945 0.014 0.065
36% 0.953 0.936 0.936 0.021 0.063
0.7
0.75
0.8
0.85
0.9
0.95
1
0.3 0.35 0.4 0.45 0.5 0.55 0.6 0.65
Threshold on Similarity Score
Segmented Mode
precision
recall
f-measure
Figure 3: Precision, recall and f-measure as functions of
threshold on tolerance factor. (Viewed better in color).
(Viewed better in color).
The graph of Figure 3 plots precision, recall and
f-measure as functions of the chosen tolerance factor,
i.e., the threshold. Claimed identities are accepted
whenever the score is above the threshold, rejected
otherwise. The higher the threshold, the higher the
precision, but the lower the recall. Table 1 shows the
results related to precision, recall, f-measure, FAR,
and FRR for values which maximize the f-measure.
The best results were achieved using a 35% tolerance
factor.
Finally, we address the computational overhead
introduced by the color classifiers considered. We
stress that the overall running time is important, since
in many applications handwritten signatures could be
decoded on low-end devices, such as mobile phones
or tablets. Figure 4 illustrates the Box-and-Whisker
plot for computational time distributions related to the
different mobile devices involved in our experiments.
Figure 4: Computational time for different mobile devices:
Box-and-Whisker plot indicating the smallest observation,
lower quartile (Q1), median (Q2), upper quartile (Q3), and
largest observation. (Viewed better in color).
The plots of Figure 4 show that even low-end
devices (such as Samsung Galaxy S2) are able to
verify the signature quickly (i.e., in a few seconds),
while devices with high performance (such as Google
Nexus 5) are really fast in verifying signatures (i.e., in
a few hundreds of milliseconds).
5 CONCLUSIONS
Our work presented a new HSV system for docu-
ment signing and authentication, whose novelties lie
mainly in the following aspects. First, we proposed
a method for the verification of signature features
which is compatible to a wide range of low-end mo-
bile devices (in terms of computational overhead and
verification accuracy) so that no special hardware is
needed. Secondly, our new method makes use of sev-
eral technical features that, to the best of our knowl-
edge, have not been previously used for handwritten
signature recognition. In our experiments, precision
and recall cross at 94%. As for the overall compu-
tational time, the average verification time is under
1 second for devices such as Nexus 5 or Xperia Z2.
This is an interesting result, especially when the lim-
ited computational power of mobile devices is consid-
ered.
REFERENCES
Andxor Corporation (2013). View2sign. http://www.view2
Handwritten Signature Verification for Mobile Phones
51

sign.com/supported-signatures.html [Online; ac-
cessed 01-April-2014].
Blanco-Gonzalo, R., Sanchez-Reillo, R., Miguel-Hurtado,
O., and Liu-Jimenez, J. (2013). Performance evalu-
ation of handwritten signature recognition in mobile
environments. IET Biometrics, 3(3):139–146.
Faundez-Zanuy, M. (2007). On-line signature recognition
based on VQ-DTW. Pattern Recognition, 40(3):981–
992.
Jain, A. K., Griess, F. D., and Connell, S. D. (2002).
On-line signature verification. Pattern recognition,
35(12):2963–2972.
Kalera, M. K., Srihari, S., and Xu, A. (2004). Offline
signature verification and identification using distance
statistics. International Journal of Pattern Recogni-
tion and Artificial Intelligence, 18(07):1339–1360.
Krish, R. P., Fierrez, J., Galbally, J., and Martinez-Diaz,
M. (2013). Dynamic signature verification on smart
phones. In Highlights on Practical Applications
of Agents and Multi-Agent Systems, pages 213–222.
Springer.
Liwicki, M., Malik, M. I., van den Heuvel, C. E., Chen, X.,
Berger, C., Stoel, R., Blumenstein, M., and Found, B.
(2011). Signature verification competition for online
and offline skilled forgeries (sigcomp2011). In Docu-
ment Analysis and Recognition (ICDAR), 2011 Inter-
national Conference on, pages 1480–1484. IEEE.
Mailah, M. and Lim, B. H. (2012). Biometric signature veri-
fication using pen position, time, velocity and pressure
parameters. Jurnal Teknologi, 48(1):35–54.
Malik, M. I., Liwicki, M., Alewijnse, L., Ohyama, W., Blu-
menstein, M., and Found, B. (2013). Icdar 2013 com-
petitions on signature verification and writer identifi-
cation for on-and offline skilled forgeries (sigwicomp
2013). In Document Analysis and Recognition (IC-
DAR), 2013 12th International Conference on, pages
1477–1483. IEEE.
Mendaza-Ormaza, A., Miguel-Hurtado, O., Blanco-
Gonzalo, R., and Diez-Jimeno, F.-J. (2011). Analy-
sis of handwritten signature performances using mo-
bile devices. In Security Technology (ICCST), 2011
IEEE International Carnahan Conference on, pages
1–6. IEEE.
Miguel-Hurtado, O., Mengibar-Pozo, L., Lorenz, M. G.,
and Liu-Jimenez, J. (2007). On-line signature veri-
fication by dynamic time warping and Gaussian mix-
ture models. In International Carnahan Conference
on Security Technology, pages 23–29. IEEE.
M
¨
uller, M. (2007). Information retrieval for music and mo-
tion, volume 2. Springer.
Piyush Shanker, A. and Rajagopalan, A. (2007). Off-line
signature verification using DTW. Pattern Recogni-
tion Letters, 28(12):1407–1414.
Qiao, Y., Liu, J., and Tang, X. (2007). Offline signa-
ture verification using online handwriting registration.
In IEEE Conference on Computer Vision and Pattern
Recognition, 2007. CVPR’07, pages 1–8. IEEE.
SutiDSignature (2013). SutiDSignature.
http://www.sutisoft.com/sutidsignature. [Online;
accessed 01-April-2014].
Trevathan, J. and McCabe, A. (2005). Remote handwritten
signature authentication. In ICETE, pages 335–339.
Citeseer.
Xyzmo (2013). Xyzmo signature solution.
http://www.xyzmo.com/en/products/Pages/Signature-
Verification.aspx. [Online; accessed 01-April-2014].
ICISSP 2016 - 2nd International Conference on Information Systems Security and Privacy
52