Off-Line Signature Verification using Thresholding
Technique
Htight Htight Wai, Soe Lin Aung
Information and Communication Technology , Principal and Associate Professor
University of Technology Yatanarpon Cyber City, Computer University (Magway),
Pyin Oo Lwin, Myanmar, Myanmar
htikehtike02@gmail.com
slinaung@gmail.com
Abstract— The off-line signature verification system includes two
parts which are training and testing. Instead of using predefined
threshold value, threshold value of each signature is calculated
by analyzing the system in the verification step. This threshold
value is used for comparing of the incoming test signature in the
testing and training part. The implemented system is developed
using Matlab (R2011a) programming tool. Experimental
researches show that the developed system has truly verified with
98.52% accuracy.
transactions. Online signature methods have high
accuracy and are very cheap to implement. Figure 1
illustrates that how the acquired the on-line
signature.
Keywords— centre of gravity, pixel density, cell angle, pixel angel
and pixel presence, Euclidean distance, Mean, Standard deviation
and Threshold value
I. INTRODUCTION
Signature verification is a major area of research
in the field of image processing and pattern
recognition. It is also widely used in the fields of
finance, access control and security. Signature
verification is the process which is carried out to
determine whether a given signature is genuine or
forged.
Unlike
character
recognition,
signature
verification consider as a complete image with
some particular curves that represent a particular
writing style of the person.
Approaches to
signature verification fall into two categories
according to the acquisition of the data: On-Line
and Off-Line.
In On-line signature verification system [1],
signatures are captured by data acquisition devices
which are a digitizing tablet, an electronic pen and a
webcam. Then, they extract dynamic features (pen
pressure, velocity and acceleration) of a signature in
addition to its shape (static). They are employed in
real-time applications for eliminating fraud. They
are used in computers for accessing sensitive data,
Forensic applications and in credit card
Figure 1 On-line Signature [1]
In on-line signature verification system,
handwritten signature is captured and recorded by
using a variety of pen-enabled devices such as
digitizing tablets, membrane touchpads, capacitive
touchpads, LCD touchscreens, computer displays or
other contact-sensitive technologies.
The most commonly used data acquisition device
is digitizing tablets and electronic pen as shown in
Figure 2.
Figure 2 Digitizing Tablet and Electronic Pen [2]
In [2], D.J.Hamilton et al. proposed a digitizing
tablet which is one of the earliest low cost devices
in 1995. Although the tablet is quite different from
paper, the signing process using digitizing tablets is
far from natural.
Therefore, this problem is solving by using
Touch-sensitive screen. This technology is
extensively used in devices such as tablet PC and
personal digital assistant (PDA), and its
performance in on-line signature verification has
been studied by F. Alonso-Fernandez et al. [3].
Moreover, a different type of device in 1983 is
developed and may also be presented by H.D.Crane
and J.S. Ostrem [4]. This device is an electronic pen,
which detects pen motion, velocity, inclination and
other properties with the electronic components
built in. To exploit electronic pen’s use in signature
verification in 1998 [5], many adjustments and
improvements have been made by A. Zhukov, M.
Vaqquez, and Garcia-Beneytez.
On-line signature verification system is widely
used around the world but our nation has not been
widely used in on-line signature verification system
into which banking and document processing. As
electronic devices are not achieved as lowest cost in
our nation and e-banking system is not developed in
our country, off-line signature verification system is
widely used till.
Moreover, on-line signature is not used in
contract signing and business trade without helping
of human person. Although on-line system is good
accuracy in verification, it has a major disadvantage
where it cannot be used for some applications that
the signer cannot be presented in the singing place.
Acquisition of off-line signature image from
document or other applications is based on two
different acquisition devices which are digital
camera and scanner.
When using digital camera, photograph or
signature image will occur illumination and
brightness of conditions. According to this
condition, digital camera is not widely used in offline signature verification system.
Unlike, ink-signed documents are required
digitization by means of a scanning device in the
off-line signature verification system [6].
pen strokes. For this reason, off-line signatures are
also referred to as static signatures. The samples of
off-line signatures are shown in Figure 3.
Off-line methods use static information for
verifying the signature. Off-line signature schemes
use the signature as the input image and are used in
the verification of bank cheques. As off-line
signatures usually have noise present, it is necessary
to apply filters to remove the noise from the
signature after processing the input image.
Researches in on-line signature verification have
been reported with high success rates. However,
off-line signature verification researches are
relatively unexplored. Although limitation of
features having from static image of signature, offline signature verification systems are still largely
in authentication of bank cheques, attendance
register monitoring and visa applications.
However, digital scanner is also used to read the
signature image on writing paper or document. The
signature image is digitized to achieve the two
dimensional image. Off-line signature verification
system includes preprocessing, feature extraction
and verification.
In digital image processing technique, two
methods, which are spatial domain and transform
domain are used to extract the features. In off-line
signature verification system, features are extracted
by using these methods. In spatial domain method,
features are extracted from the whole image or subimage part.
J.J. Brault and R. Plamondon [7] segmented
handwritten signatures at their perceptually
important points. Then, the geometric features are
based on two sets of points in two-dimensional
plane. Each set having six feature points which
represent the stroke distribution of signature pixels
in image. These twelve feature points are calculated
by Geometric Center.
Depending on Geometric Center, twelve feature
points are extracted using two techniques which are
horizontal and vertical splitting techniques in
Banshider Majhi, Y Santhosh Reddy and D
Prasanna Babu [8]. This paper occurs that signature
image is moved to the center of image before
extracting the feature points. Although two
Figure 3 Samples of off-line signatures [6]
Therefore, the obtained signature image only threshold values are used for horizontal and vertical
provides the coordinates of pixels representative of splitting techniques in verification of signature,
large feature points are needed to extract in this
paper. If feature points are large, good performance
can be occurred.
Geometric features which are based on shape and
dimensions of the signature image are described by
Ashwini Pansare and Shalini Bhatia [9]. Although
individual sixty feature points are extracted in both
vertical and horizontal splitting techniques, large
amounts of training data are used for classification
of image in verification of offline signature using
neural network. Therefore, weight values are
chosen and computation is complex. As using large
amount of training data, processing time is so long.
In another paper [10], signature image is split to
achieve 64 sub-images. Each sub-image extracts
three features, which are cell size, image centre
angle relative to the lower cell corner and pixel
normalized angles relative to the lower cell corner.
As single threshold value is used for all features,
performance accuracy is fail.
Without retrieving of the whole signature,
horizontal and vertical projections based features
were extracted by Hifzan Ahmed and Dr. (Mrs.)
Shailja Shukla [11]. Unlike above paper, one
dimensional feature is extracted. This paper occurs
that one dimensional projection based feature is less
accuracy than combination of both the projections
feature in verification.
Verification and recognition techniques in offline signature verification system are using such as
thresholding
technique,
Euclidean
distance
classifier, Support Vector Machine, Hidden Markov
Model, Neural Network and so on.
Threshold selection technique, is based on
statistical parameters like average and standard
deviation, is used in Banshider Majhi [8] for
verification of off-line signature. In this paper, two
threshold values are used for vertical and horizontal
splitting techniques. Median and standard deviation
values are used to evaluate the threshold values. In
verification phase, threshold value of test signature
is compared with the threshold value of training
signatures. This paper occurs that a verification of
off-line signature is depending on the two threshold
values.
Dr. Daramola Samuel and Prof. Ibiyemi Samuel
[10] used threshold value and Euclidean distance
classifier for verification of offline signature. This
paper occurs that signature image is split to achieve
64 sub-images. Then, each sub-image extracts three
features, which have three threshold values for the
whole signature image. This paper did not use
threshold values for each cell. This paper needs to
detect all kinds of forgeries particularly in paper
documentation environment, like banks, schools
and government ministries.
Verification and recognition techniques are
described by the above previous related papers. The
proposed system aims to use threshold value for
verification of off-line signature.
The objective of the proposed system is to
implement for offline signature verification using
thresholding technique.
This paper is organized as follow: in section 2,
implementation of the proposed system is reviewed.
Section 3, is the experimental results. The last
section of this paper is about the conclusion.
II. IMPLEMENTATION OF THE PROPOSED SYSTEM
Table 1 gives an algorithm for the Off-line
Signature Verification System in which threshold
value is used for verifying the authenticity of
signatures.
TABLE 1 Algorithm for Off-line Signature
Verification System
Input: signature from a database
Output: verified signature classified as accepted or
rejected.
1. Select signature image from a database.
2. Preprocessing the signatures.
2.1.
Converting image to binary.
2.2.
Image filtering
2.3.
Thinning
2.4.
Rotation normalization
2.5.
Finding bounding box of the
signature.
2.6.
Resizing the signature.
3. Feature extraction
3.1.
Divide into four sub-image part.
3.2.
Dividing into sixteen sub-image
parts.
Repeat into 3.1.
3.3.
Dividing into sixty-four sub-image
parts.
Repeat into 3.2.
3.4.
Extract four features
3.4.1. Pixel Density feature
3.4.2. Cell Angle feature
3.4.3. Pixel Angle feature
3.4.4. Pixel Presence feature
4. Verification
4.1. Training
4.1.1. Find median values of all features in each cell
for all sample signatures.
4.1.2. Find distance between median value
of all features and feature vector of sample
signature.
4.1.3. Find average distance for all features
of sample signatures.
4.1.4. Find standard deviation for all
average distance of all features of sample
signatures.
4.1.5. Find threshold value by using average
distance and standard deviation of sample
signatures.
4.2. Testing
4.2.1. Find distance value between median value of
sample signature and feature value for test
signature.
4.2.2. Find threshold value by using square root of
sum of power of distance value.
5. Comparison
5.1. Compare between threshold value of training
signature and threshold value of testing signature.
5.2. Shows the result for Off-line Signature
Verification System.
III. EXPERIMENTAL RESULTS
The proposed system uses 700 sample signatures
for 70 persons in Database. Each signature is
written on paper and pen color is blue. The
proposed system includes two parts which are
training and testing.
For training, 350 sample signatures are used. In
testing part, 700 signatures of 70 persons are used.
For training part, 350 sample signature images for
70 persons are used but 700 signature images are
testing for our proposed system.
The figure 4 shows the some of the sample
signature images for training signature database.
Figure 4 Some of the sample signature database
Similarity Analysis of four persons among 70
persons is shown in Table 2. In this table, 5
signatures of each person in training part are tested
with the 10 signatures for each same person and
each signature’s similarity percentage is calculated.
Table 2 Similarity Analysis of Sample Signatures
for each person
are tested and accuracy occurs 100 percentages of
training stage. But, 700 signatures of 70 persons in
testing are tested with the same persons of
signatures. The acceptance rate of all signatures is
98.52 percentages.
Table 3 Performance Result of Acceptance Rate
In Table 4, Performance Result of Rejection Rate
for off-line signature verification system is shown.
In Training, 350 sample signatures for 70 persons
are tested and accuracy occurs 100 percentages of
training stage. In testing part, three signatures for
each unknown person are used and therefore 70
unknown persons have a total of 14490 signatures.
But, overall true rejection rate is 96.62 percentages
for 14490 signatures of unknown persons.
Table 4 Performance Result of Rejection Rate
IV. CONCLUSIONS
In splitting the signature image, instead of using
the centre of space, the centre of gravity of the
image according to the pixels is used so that
presence of signature pixels in each splitted image
is balanced. The total number of splitted images is
64. Four features are extracted from each splitted
image and so the total number of features for each
signature is 4 x 64.
Performance Result of Accepted Rate for off-line
In verification of the signature, Euclidean
signature verification system is shown in Table 3. distance, median values and thresholding
In Training, 350 sample signatures for 70 persons techniques are used. The implemented system has
98.52 percentages of True Acceptance (i.e 1.48 %
of FAR)and 96.62 percentages of True Rejection
(i.e 3.38% of FRR). In comparison with the existing
off-line signature verification systems, the proposed
system is better not only from the point of view of
the amount of data analysis (700 signatures are
analyzed) but also from the point of view of
detailed analysis (analysis of acceptance, analysis
of rejection, analysis among genuine signatures,
analysis among false signatures, analysis between
genuine signatures and false signatures). The
accuracy of the system is also acceptable and it is
recommended that the proposed system can be used
in any field of signature verification especially for
the bank cheques, vouchers, certificates, wills and
document processing with the human’s signatures.
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