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REVIEW OF VARIOUS TECHNIQUES OF DIGITAL
SIGNATURE VERIFICATION
Miss. P.N .Ganorkar
Rajiv Gandhi College Engineering and
Research
R.T.M.N.U Nagpur (Maharashtra), India
Email:
prachiti.ganorkar@gmail.co
Miss. Kalyani Pendke
Rajiv Gandhi College Engineering and
Research
R.T.M.N.U Nagpur (Maharashtra), India
Email:
pendke@gmail.com
ABSTRACT: In the era of growing technology, security is the
major concern to avoid fakes and forgeries. There are various
Biometric systems which help in personal identification, amongst
those verification systems, one system is Signature Verification
System, Signature verification is split into two according to the
available data in the input. Online and Offline. It also referred as
static and dynamic. Signature verification also used to provide
authentication to user. The main advantages of signature
verification are that it used for e-business which helps in banking
applications. The proposed paper present review of various
techniques of digital signature verification and also various
algorithms for signature verification.
Keywords: Segmentation, Hidden Marko Method, Offline
signature algorithm, online signature algorithm, Support Vector
Machine. Relative slope algorithm.
I. INTRODUCTION:
Signature verification techniques utilize many different
characteristics of an individual’s signature in order to identify
that individual V The advantages of using such an
authentication techniques are:
i) Signatures are widely accepted by society as a form of
identification and verification.
ii) Information required is not sensitive.
iii) Forging of one’s signature does not mean a long-life loss
of that one’s identity.
The basic idea is to investigate a signature verification
technique which is not costly to develop, is reliable even if the
individual is under different emotions, user friendly in
terms of configuration. In signature verification application,
the signatures are processed to extract features
that are used for verification. There are two stages called
enrolment and verification.
Mrs. Shailendra Aote
Rajiv Gandhi College Engineering and
Research
R.T.M.N.U Nagpur (Maharashtra), India
Email:
shailendra_aote@rediffmail.com
In determining the performance of the verification system the
selection of features takes main role and it is critical. There
are two types of features that validating a signature. They are
static and dynamic features. Static features are those, which
are extracted from signatures that are recorded as an image
whereas dynamic features are extracted from signatures that
are acquired in real time. For signature verification, the
system uses signature which is already present in database and
the real time signature taken by the digital pen and pad. Slope
value of stored signature and real time signature taken by
digital pen being match for verification. If value matches up to
some range then it will be considered as genuine otherwise
forgery. Other parameters for verification are time, speed,
threshold value of both the signature, efficiency, accuracy and
the various processes are data acquisition, pre-processing,
enrolment, feature extraction, relative slope based extraction,
two tier time extraction, verification algorithm, hidden
Markova method.
.
II. LITERATURE REVIEV:
2.1 Various Techniques Used For Signature Verification
2.1.1 Hidden Markov Models Approach
Hidden Markov Model (HMM) is one of the widely used
models for sequence analysis in signature verification
techniques in trajectory Handwritten signature contain
sequence of vector of each point value related to signature in
trajectory. Therefore a well chosen set of feature vector for
hmm could used to design an efficient signature verification
system. Stochastic model have the capacity to observe the
variability between pattern and their similarities. HMM
stochastic model contain the matching model and signature
.This matching is done on the basis of
probability of distributed features of signature or probability
of how signature is calculated. The probability of result
signature is higher than test signature then signature is genuine
of original person otherwise forgery. Paper [1] describes the
system that use only global feature. it uses binary signature
pattern .a discrete random transform is calculated for each
binary signature image at the range of 0-360,which is a
function to calculate total pixel in the image and the intensity
of given pixel is calculated using non-overlapping beams per
angle for x no. of angle. due to this periodicity ,it is shift
,rotation and scale invariant .this HMM model is used for each
writer signature this method achieve an 18.4% AER for a set
of 440 genuine signature from 32 writer and 132 skilled
forgeries. Using this is method we can divide the line into no.
of segment for calculating the values of various algorithms.
Hidden Markova model again used two models User-Specific
Model (US.HMM) And User Adapted universal background
model (UA-UBM HMM). US-HMM and UA-UBM systems
can be used together for improved verification performance by
fusing at the score level the Viterbi path information from the
US-HMM system and the likelihood ratio evidence from the
UA-UBM system.
images were used and with the full estimated convenience
matrix incorporated.
2.1.4 Support Vector Machine
Support vector machine heaving algorithm that uses the
high dimensional features space and estimate difference
between classes of a given data to generalize unseen data .for
classification and verification purpose, the system [5] uses
global directional and grid features of the signature. and SVM
for classification and verification. The database of 1320
signatures is used from 70 writers. 40 writers are used for
training with each signing 8 signatures thus a total of 320
signatures for training. For initial testing, the approach uses 8
original signatures and 8 forgeries and achieves FRR 2% and
FAR 11%.support vector machine used various techniques
like LCSS-GLOBAL and LCSS-LOCAL.LCSS is nothing but
local common subsequence used to find the approximate
value of verified signature. The two kernels LCSS-global and
LCSS-local offer the possibility to classify time series of
different lengths with SVM technique. SVM used dynamic
warping technique. This technique used algorithm for
alignment of time series function. [6]
2.1.2 Neural Networks Approach
Because of power and ease, NN is popular in pattern
Recognitions .the simple approach for this is to first extract
the set of features representing the signature which provide
different sample from several sample .the 2nd step is for NN
to learn the relationship between signature and its class either
genuine or forgery. Once this relationship has been learn the
network represent with test signature that can be classified as
belonging to a particular signature. Therefore NN are highly
suitable for global aspect of handwritten signatures.
Theproposed paper [2] uses structure features from the
structure contour, modified direction feature and additional
features like surface area, length skew and centriod Feature in
which a signature is divided into two halves and for each
halves a position of centre of gravity is calculated in reference
to the horizontal axis for classification and verification ..Two
approaches can compared the Resilient Back propagation
(RBP) and neural network and Radical Basic Function (RBF)
using database of 2106 signature containing 936 genuine and
1170 forgeries. These two classifier register 91.21% and 88%
true verification respectively.
2.1.3 Template matching approach
Fang et al [3] proposed two methods for the detection of
skilled forgeries suing template matching. One of them is
depend on the optimal matching of the one dimensional
projection profiles of the signature pattern and the other based
on the elastic matching of the strokes in the two dimensional
signature pattern. The testing of the signature verification is
given by the statistics of the training set compared with the
positional variation and the decision based on a distance
measures is made . Both the type of images is tested i.e. binary
and gray .the error rate of 18.1% of the average verification
was achieved for matching purpose when local pokes of the
vertical projection profiles of the gray level signature
III.ALGORITHM USED FOR SIGNATURE VERIFICATION.
3.1.1 Relative Slope Algorithm
It is one of the algorithms used to verify the signature.
This algorithm based on slope value of the signature [7]. It
uses HMM model to calculate and optimized the slope value
.HMM model used to divide the line into segment which helps
us to calculate the slope value of line easily using previous
value. This algorithm again uses relative slope extraction
algorithm and two level time metric algorithm and verification
algorithm. Various types of signature like offline signature,
online signature used relative slope algorithm to calculate
slope value and match them with another value to verify
whether the given signature is forgery or genuine. algorithm
and verification algorithm. Various types of signature like
offline signature, online signature used relative slope
algorithm to calculate slope value and match them with
another value to verify whether the given signature is forgery
or genuine This algorithm gives efficiency and accuracy
higher than other to calculate slope value. Various processes
like data acquisition, pre-processing, feature extraction,
enrolment, and verification etc. Following are the steps of
algorithm.
Relative Slope Algorithm
Steps:
1) Pre-process and normalize the algorithm.
2) Divide the signature into segment using optimized HMM
method.
3) Based on requirement combine these segments into line
segment.
4) Calculate the relative slope value of each segment with
respect to previous segment.
5) Carry step (4) till all segment are processed else step (6).
6) Store the slope value of each segment which can be used
for verification.
7) End.
IV. CONCLUSION:
The above paper presents the survey of various techniques
of digital signature verification. These various techniques
provide security and authentication to the user. The above
paper also presents various algorithms to provide signature
verification. The various techniques are NEURAL
NETWORK which provide various parameters for matching,
TEMPLATE MATCHING APPROACH which provide
matching technique like elastic and dynamic matching, it
provide minimum error rate and SUPPORT VECTOR
MACHINE which provide vector representation data and
reduced error rate and HMM MODEL .From all of the above
the HMM model is one of the popular technique for signature
because it divide the given signature into no. of segment and
also gives low error rate.
V.REFERANCE
[1] S.Srihari. K. M. Kalera. And A. XU, “Offline Signature
Verification and Identification Using Distance Statistics,”
International Journal of Pattern Recognition And Artificial
Intelligence, vol. 18, no. 7, pp. 1339–1360, 2004.
[2] H. S. Sridhar and M. Beall, “Signature Verifications Using
Kolmogrov Smirnov Statistic,” Proceedings of International
Graphonomics Society, Salemo Italy, pp. 152–156, June, 2005.
[3] Ashwini Pansare, Shalini Bhatia, “ Handwritten Signature
Verification using Neural Network,” International Journal of Applied
Information Systems (IJAIS) – ISSN : 2249-0868 Foundation of
Computer Science FCS, New York, USA, January 2012.
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verification based on global features” IEEE International Advance
Computing Conference, 2009.
[5] Martinez, L.E., Treviso, C.M, Alonso, J.B., and Ferrer, M.
Parameterization of a forgery Handwritten Signature Verification
using SVM. IEEE 38thAnnual 2004 International Carnahan
Conference on Security Technology, 2004 PP.193-196
[6] Christian Gruber, Thyme Gruber, “Online Signature Verification
with Support Vector Machines Based on LCSS Kernel Functions”,
IEEE, August 2010
[7]Sudarshan Madabashi, Vivek Shriniwas,” Online Offline
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[8] H. Morita, T. Ohishi, Y. Komiya, “On-line Signature
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[9] Enrique ArgonesRua, “Online Signature Verification Based on
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