International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 6 - Jun 2014
Comprehensive Review of Offline Signature
Verification
#1
Rajinder Kaur, #2 Neha Pawar ,#3 Amit Chhabra
#1
Student Mtech, #2 AssistanceProfessor, #3 Head of Deptt.
Computer Science Department
Swami Devi Dyal Institute of Engineering and Technlogy, Barwala,Panchkula
Krukshetra university, krukshetra.Hyrayna-126102 India
Abstract— In this paper we review the feasibility of combining
continuous base classifiers for the purpose of off-line signature
verification. The verification of signatures by taking the
credentials of size and angle invariant for cheque system, there is
probability of mismatch the characters in the signature of the
user, here the Optical Character Recognition is used for
automatic signature verification which can be helpful in many
financial and business systems, but sometimes due to issues in
colors of the character, font size, abstract or isolated character or
symbol would be result into the mismatch of the signature, so our
methodology for verification of signature is quite concrete to
overcome such cases.
NO.
Off-line verification
Online-verification
1.
Verification
depends
upon analyzed image of
person’s signature
2.
Having a lot of noise
included
Information
obtained
slightly
Fast verification process
Verification
depends
upon
capturing
and
analyzing the real time
signature
as
person
signing it
Zero noise included
3.
4.
Key
words—DTI(Decision
Tree
Induction),NN(Neural
Network),Sign(Signature),Diff(Differences),Pic(Picture).
I.
Fairly high degree of
accuracy
Table 1: Comparison of Off-line Verification and On-line Verification
INTRODUCTION
Signature verification is process of matching the signature in
database with sample of signature given to be verified.
Handwritten signature is widely accepted personal attributes
for identity verification. The main area of research on
signature verification is in the field of pattern recognition and
image processing. It is also vastly used in the fields of finance,
access control and securitythe signature verification is of two
types:1) Offline verification 2)Online verification. In off-line
verification system verification is done by off-line. The data
is accessed by scanning individual handwritten signature. That
scanned sign will be used for signature verification process.
In on-line verification system verification is done on-line. The
data accessed by touch screen, digitizer and stylus. This
machines will make dynamic values such as location, pen
pressure, co-ordinate values, speed of signature or time etc.
In Off-line mode, it is complex to divide signature strokes due
to highly stylish and unconventional writing styles. All of the
complexity joined causes large intra-personal variation among
sign .Biometrics offers many benefits over more commonly
used authentication methods which provide a wide data
material for study. More common such as photo ID cards or
magnetic stripe cards can be lost, stolen, duplicated or even
just left at home. Another common means of authentication is
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5.
Information obtained by
varying
Faster
than
offline
verification
Very high degree of
accuracy
the use of passwords. There various things that can be go
wrong with a password and in today’s fast paced world,
people are enforced to remember a multiple of passwords for
many things. Biometrics is fast to use, easy to use, accurate
and authenticate. We need to use certain classifier that is
going to play the role of decision making for verification. This
phenomenon is termed as classification and is being done on
certain criteria i.e. features that need to extracted from scan
image of the sign to be verified.
II.
FEATURE EXTRACTION
In this classification process, we need to look for certain
criteria i.e. decide condition for matching signature sample
with signature record in the database.
This criteria is the feature linked to scanned sample of image of
signature.A digital Image can be describe as two dimensional
image as a finite set of discrete values, known as pic elements
or pixels. Pixel ideals typically represent grey levels, color
Intensity, heights, opacities etc. Image noise is the random
variation of brightness or color information in images produced
by the sensor and circuitry of a scanner or digital camera.
Image noise is considered as an undesirable by-product of
image capture. Image Processing is the analysis of a picusing
predefined set algorithm let us know more detail information
about the digital image.[3]
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 6 - Jun 2014
Due to the nature of static features and the adverse effect of
background noise in scanning image. The offline systems
made use of the certain characteristics of signature. The
incorporation of intelligent image processing and feature
extraction techniques, as well as robust classification models,
are therefore key to the success of any off-line verification
system.
A.FORGERIES
In the context of off-line signatures, forgeries may generally
be categorised as eitherrandom, simple or skilled, in increasing
order of quality. Furthermore, skilled forgeries may be subcategorised as either amateur or professional, as illustrated in
Figure below .
[4]
Forgery
Figure 2: Typical examples of (a) a genuine signature, as well as (b)
professional skilled, (c)amateur skilled and (d) random forgeries.
B.FEATURE EXTRACTED
Random
Simple
Skilled
Amateur
Professional
Figure1: Classification of forgeries
1) Random forgeries: Random forgeries encompass any
arbitrary attempt at forging a signature, generally without
prior knowledge of the owner’s name. This category of
forgery may be random open strokes and easy to detect.
The features of an image like pixel intensity, pixel no., pixel
position, pixel area. Following feature could be extracted:
1) Pixel Intensity: The pixel density feature xPD ∈ [0, 1] has
been used to great effect in such works asJustino et al. (2001)
and Oliveira et al. (2005). Since the pixel density of a
signature segment is directly linked to stroke width, it is also
commonly referred to as apparentpen pressure. The pixel
density of an image J is obtained by computing the
ratio of pen stroke pixels to total image pixels, or
(1)
2)Simple forgeries: Simple forgeries are used when the
forger’s knowledge is restricted to the name of the signature’s
owner. Due to the arbitrary nature of signature design, simple
forgeries may in some cases bear an alarming resemblance to
the writer’s genuine signature. In such cases, more
sophisticated systems, able of detecting subtle stylistic diff.,
are required in order to distinguish between genuine signatures
and forgeries of this type.[6],[9],[2]
3)Skilled forgeries: In some instances, the forger is not only
familiar with the writer’s name, but also has access to samples
of genuine signatures. Given ample time to practice signature
reproduction, he is able to produce so-called skilled forgeries.
The vast majority of skilled forgeries may be categorized as
amateur, as this type of forgery may be produced by any given
individual. In contrast, to produce a professional skilled
forgery, the forger typically requires a certain amount of
knowledge regarding forensic document analysis. The
duplication of the original signature, which means is that the
person recognizes that how the original signature exactly
looks like .So we can say that skilled signatures are the most
tuff to detect.
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2)Gravity Center: The gravity center distance feature xGCD
∈ [0, 1], as described in Justino et al. (2005), is
Obtainedin two phases. Firstly, the coordinates of the image
centroid (¯x, ¯y) are computed using
(2)
Thereafter, the gravity center distance associated with J, as
illustrated in Figure 3.4, can
be computed as follows,
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(3)
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 6 - Jun 2014
[4]
Figure 4: The perceptron can learn by adjusting theweights to approach the
desired output.
Figure 3: Computation of the gravity centre distance feature.
3)Image Area: It is defined as no of pixels in width and no of
pixels in height of an image. let us assume that X is no of
pixels in width of Image and Y is no of pixels in Height of an
image, or
Image Area = X x Y
(4)
III CLASSIFIERS
Classification is a method by which labels or class identifiers
are attached to the pixels making up a remotely sensed image
on the basis of their characteristics. These characteristics are
generally measurements of their spectral response in different
wavebands. They may also include other attributes (e.g.,
texture) or temporal signatures. In order to combine the
classifier ensemble constructed from these base classifiers,
several classifier combination strategies is considered.
Neural Network algorithm of self learning is investigated. The
efficiency of this method is greatly increased by the sigmoidal
score normalization function utilized in this study. The other
decision classifier Techniques Are Also Investigated, Namely
The Neural Networks Classifiers and Decision Tree Induction
Classifiers The popular classifier ensemble combination
strategy. In constructing a classifier ensemble, any number of
base classifiers may be utilized, regardless of their feature
extraction or signature modeling techniques. The optimal
ensemble composition is determined experimentally.
X1<=n1
X4<=n4
Neural networks do not follow a set of instructions, provided
for them by the author, but they learn as they go case by case.
Neural networks are highly devoted when trained using a large
amount of data. They are used in applications where security
is highly valued. In this research we used Multi-Layer
Perceptron MLPs neural network. The structure is depends of
the neural network on all the layer providing significant
feedback, where all the nodes are connected to all other nodes
in the next layer and so on, but these nodes do not have any
connections with the layer below it. Then, it was being
modified to function as a back propagation neural network,
using the BP algorithm.[7]
X3<=n31
Y=3
X3<=n32
Y=1
Y=3
X2<=n2
Y=2
Y=1
A NEURAL NETWORKS CLASSIFIERS
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B.DECISION TREE INDUCTION CLASSIFIERS
An alternative approach is to use a multistage or sequential
hierarchical decision scheme. The basic idea involved in any
multistage access is to break up a difficult decision into a set
of several simple decisions, hoping the final solution achieved
in this way would simulate the expected desired solution.
Hierarchical classifiers are special type of multistage classifier
that allows rejection of class labels at intervening stages.
Y=1
X3<=n3
Y=3
Figure 5: A DTI classification tree for a five dimensional features and three
classes.[4]
Classification trees present an effective implementation of
such hierarchical classifiers. Classification trees have become
progressively important due to their imaginary simplicity and
computational efficiency. A decision tree classifier has a
simplest form which can be easily compressed then stored in
storage device and that can accurately and sufficiently
classifies new data. [1],[5]Decision tree classifiers can act
automatic feature selection and difficulty reduction, and their
tree structure gives easily understand and intelligible
information about the predictive or generalization ability of
the classification. To construct a classification tree by
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International Journal of Engineering Trends and Technology (IJETT) – Volume 12 Number 6 - Jun 2014
heuristic approach, it is assumed that a data set expressing of
feature vectors and their similar class labels are available. The
features are recognized based on problem specific knowledge.
The decision tree is then composed by repeat partitioning a
data set into further more parts, more homogenous parts on
the basis of a set of tests applied to one or more attribute
values at each branch in the DTI tree.
This procedure has three steps: splitting nodes, determining
which nodes are terminal nodes, and assigning class label to
terminal nodes.[10]
In general, a pattern recognition system is constructed by
utilizing one or more feature extraction techniques, in
conjunction with a single classification technique. The use of
several feature extraction techniques is recommended, as this
ensures greater separation of different pattern classes in the
feature space. Given a set of two or more classifiers, referred
to as a classifier ensemble, it is logical to expect an
improvement in performance when combining the separate
efforts of each into a single classifier, referred to as a
combined classifier. The combination process is performed
either on score level or decision level.
IV CONCLUSION
We here analysed and conferred that an off-line signature
verification system which is based on simple geometric
features. The geometrical features were chosen carefully so as
to sufficiently distinguish signatures from different people.
The study show that using a localized threshold out performs a
global threshold by a vast margin. The localized thresholds
ability to correctly classify a signature is up by more than the
global threshold. The false acceptance rate is down when
using localized thresholds. Improved performance may be
obtained by using a higher number of features as well as
incorporating the capture of dynamic data as it is this dynamic
data that uniquely identifies a person. The system can be
improved using the concepts of Neural Networks and DTI
Algorithm which hold a lot of promise of being able to
organize a system that performs with a very high level of
accuracy.
V FUTURE SCOPE
likely be achieved by either considering a fundamentally
different set of features and/or signature modeling techniques.
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[8] Jacques philip swanepoel, bschons (stell),off-line signature verification
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[9] Coetzer, j., herbst, b. and du preez, j. (2004). off-line signature verification
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During the course of this reviewing, certain concepts were
encountered that warrant further investigation, as they are
deemed potentially beneficial to the systems developed in
thisstudy. These concepts are not included in this work, either
due to being considered outside the scope of this study, or
simply due to time constraints.These topic were Adaptive grid
segmentation and Conditionally independent classifier
ensembles. The flexible segmentation grids could be
developed in this study, however, are subject to the
requirementthat each grid cell boundary is dilated, if possible,
to exactly the same extent. Definitely warrants further
investigation to attempt the design and implementationof such
a conditionally independent classifier ensemble. The
requirement of independent classifier decisions may most
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