Offline handwritten signature identification using adaptive window positioning techniques

Signal & Image Processing : An International Journal (SIPIJ) Vol.5, No.3, June 2014
DOI : 10.5121/sipij.2014.5302 13
OFFLINE HANDWRITTEN SIGNATURE
IDENTIFICATION USING ADAPTIVE WINDOW
POSITIONING TECHNIQUES
Ghazali Sulong, Anwar Yahy Ebrahim and Muhammad Jehanzeb
UTM-IRDA Digital media centre (MaGIC-X), Faculty of Computing, Universiti
Tecknologi Malaysia (UTM) Skudai 81310, Johor, Malaysia
ABSTRACT
The paper presents to address this challenge, we have proposed the use of Adaptive Window Positioning
technique which focuses on not just the meaning of the handwritten signature but also on the individuality
of the writer. This innovative technique divides the handwritten signature into 13 small windows of size nxn
(13x13). This size should be large enough to contain ample information about the style of the author and
small enough to ensure a good identification performance. The process was tested with a GPDS dataset
containing 4870 signature samples from 90 different writers by comparing the robust features of the test
signature with that of the user’s signature using an appropriate classifier. Experimental results reveal that
adaptive window positioning technique proved to be the efficient and reliable method for accurate
signature feature extraction for the identification of offline handwritten signatures .The contribution of this
technique can be used to detect signatures signed under emotional duress.
KEYWORDS
Offline Handwritten Signature, GPDS dataset, Verification, Identification, Adaptive window positioning.
1. INTRODUCTION
The signature of any person is an important biometric characteristic which is usually implemented
for personnel identification or document authentication. An increasing number of financial and
business transactions are approved via signatures [1]. Handwritten signature as a method of
authentication has become a part of everyday life, and there is probably not a single area where it
is not used. Handwritten signature usage dates from ancient times and is held until today as a
means of giving consent to something that needs to be done. The problem arises when someone
decides to imitate the signature of the person with the purpose of fraud or false representation.
Therefore, there is need for adequate protection personal signatures. One good method of
protection is by the use of biometric systems based on handwritten signature [2].
Handwritten signature is defined as the first and last name written in your own handwriting [3]. It
is often the case that the signature does not contain the full name but only one part of it, or
sometimes a set of connected lines that do not resemble the name of the signer. This type of
signature is called paraph, and is defined as an abbreviated signature, sometimes only the initial
letter of the name, and placed on administrative act within the routine procedure, which means
that whoever put the initials agrees to endorse the content. Work on signature can be carried out
by using either signature identification or signature verification. Signature Identification can be
done using a person's identity based only on biometric measurements. Signature verification is the

14
process used to recognize an individual’s handwritten signature for genuine or duplicate/forgery
[4]. Signature identification and verification system are broadly classified in two ways: on-line
and off-line. In an off-line technique, signature is signed on a piece of paper and scanned into a
computer system. In an on-line technique, signature is signed on a digitizer and dynamic
information like speed, and pressure is captured in addition to a static image of the signature.
Verification decision is usually based on local or global features extracted from the signature
being processed. Excellent verification results can be achieved by comparing the robust features
of the test signature with that of the user’s signature using an appropriate classifier [5].Due to the
relative ease of use of an offline system, a number of applications worldwide prefer to use this
system (e.g. Check verification in the bank) [6]. Even though the dynamic signature verification
is more reliable as compared to offline systems in terms of accuracy, this online method requires
special hardware digitizers, and pressure sensitivity tablet to capture the dynamic features, which
the offline method does not require [7]. For an offline method, the extracted features of the
individual’s signature consider as an input to the system, which is then processed based on some
predefined standardized methods. After the standardization stage, called pre-processing, extracted
features from the offline handwritten signature are then identified and verified against a stored
data set through a process known as graph matching. However, researchers in this field have
failed to take into account external factors that may influence the signature of an individual as the
individual may be under different emotions when signing his signature [8] [9] [10]. Thus, an
individual though having a unique signature may sign a signature that can be in different shapes
under different situations. Thus, it is important to take into account external factors when
investigating a signature verification technique. This paper therefore attempts to address this
challenge for offline handwritten signature identification by proposing a new technique based
adaptive window positioning method for signature feature extraction that will give reliable
accuracy in verifying an individual’s signature even when the user is under different emotions
since signature verification applications are used in our daily lives and will be exposed to human
emotions. This proposed technique improves the efficiency and accuracy of offline handwritten
signature verification and identification system. It also has a more enriched process of signature
feature extraction by using the adaptive window positioning method. This gives it the ability to
verify and identify an individual’s signature even when signed under emotional stress.
Furthermore, this study creates room for further research into the application of this technique in
offline handwritten signature verification. The paper is organized as follows: Section 2 presents a
review of related work to the study. Section 3 describes our proposed method. Section 4
discusses the results of our experiments using GPDS dataset. Section 5 concludes the paper.
2. BACKGROUND OF THE STUDY
Broadly, features can be classified as global or local, where global features represent signature’s
properties as a whole and local ones correspond to properties specific to a sampling point [11].
There are several feature extraction techniques employed in signature verification and
identification systems [12] [13]. The basic purpose of any feature extraction is to extract accurate
features from any given sample. The amount of feature extracted is irrelevant to the overall
accuracy of the result as fewer features may be extracted using any techniques, and yet yield
better result than one in which a large number of features are extracted. Thus the focus locus is
the technique employed. Global and local features contain information, which are effective for
signature recognition. Selection of different features is vital for any pattern recognition and
classification technique [14]. Global signature features are extracted from the whole signature
image. On the other hand, local geometric features are extracted from signature grids. Moreover,
each grid can be used to extract the same ranges of global features. The Combination of these
global and local features is further used to successfully determine the identity of authentic and
forged signatures from a database. This set of geometric features is
Used as input to the identification system.

15
Table 1. List Different Offline Handwritten Signature Identification and Verification Methods
S/N Approach Characteristics Advantages Disadvantages
1. Template
Matching
approach
- Employs pattern comparison
process
- Suitable for detecting
genuine signatures via
rigid matching
- Not appropriate
for detecting
skilled forgeries
2. Neural
Networks
(NN)
approach
- Learns by example thus good
for learning the underlying
structure of the data set.
- Can be used to model
complex functions
- Highly suitable for modeling
global features of handwritten
signatures
- Widely accepted
classifiers for pattern
recognition problems
- Has very low FAR and
FRR results
- Not very
suitable for
modeling
statistical and
geometric features
- Requires a
highly
representative
data set
3. Hidden
Markov
Models
(HMM)
approach
- Best suited for sequence
analysis in signature
verification
- Uses stochastic matching
(model and signature) to
extract variability between
patterns and their similarities
- Has various topologies and
adopts probability density
function modeling in its design
for the verification task
- Can easily detect simple
and random forgeries in
signature verification
- Very poor in
detecting skilled
forgery
4. Statistical
approach
- Employs statistical method to
determine the relationship,
deviation, etc between two or
more data items
- Uses the concept of
Correlation Coefficients
- Good at identifying
random and simple
forgeries
Its :graphometry -based
approach avails so many
usable features for
signature verification, e.g.,
calibration, proportion,
guideline and base
behavior
- Its use of static
features limits it
from detecting
skilled forgery
5. Structural
and
Syntactic
approach
- Uses symbolic data (e.g.
Signatures) structures such as
strings, graphs, and trees to
represent recognition patterns
- Employs the use of a
Modified Direction Feature
(MDF) to extract transition
locations
- Appropriate for detecting
genuine signatures and
targeted forged signatures
- Very exhaustive
method as it
requires large
computational
efforts and
training sets
6. Wavelet-
based
approach
- It is a multi-resolution
transform that can decompose
a signal into lowpass and
highpass information
- Wavelet theory is employed
in decomposing a curvature-
based signature into a multi-
resolution signal
- Can be applied in both
offline and online
signature verification
- Can decompose a
curvature-based signature
into a multi-resolution
format
- Can be applied to
symbolic languages such
as Chinese and Japanese
besides English

16
The global features that are extracted from the signature sample include [13]: Width (Length),
Height, Aspect ratio [15], Horizontal projection, Vertical projection, Area of black pixels,
Normalized area, Center of gravity, Maximum and minimum black pixels in vertical projection,
Maximum and minimum black pixels in horizontal projection, Global baseline, Upper and lower
edge limits, Middle zone, Hough transform [16], Curvelet transform. Local features- also known
as Grid features can be extracted from gray level, binary and thinned signature images. From the
small regions of the whole image, local features are estimated, such as center of gravity, width,
height, horizontal and vertical projections, aspect ratio, area of black pixels of each grid region,
normalized area of black pixels, gradient and concavity features etc . The global features can also
be considered as local features for each grid region. To obtain a set of global and local features,
both of these feature sets are combined into a feature vector and the feature vector is sent as input
to the classifiers for generating matching scores [16] [2]. Other offline signature features include
Statistical features [10], Geometry and topological features [17], Kurtosis [18], Orientation [18],
Gabor Wavelet [19], Eccentricity [18], Skewness [15], Discrete Wavelet Transform (DWT)
Features [20], Modified Direction Feature [15], and Contour let transform (CT) [21] [15].
3. PROPOSED METHOD
In this section, we present our method and its application processes to offline signature feature
extraction using the adaptive window positioning technique. Thus, the section illustrates the
proposed methodology for the offline handwritten signature identification system using adaptive
widow positioning technique. Here, we follow through all the logical steps involved starting from
getting the handwritten offline signature image to the signature identification stages. Figure 1
presents a flowchart of our proposed system.
Figure 1. Flowchart of offline signature identification process using window positioning
technique

17
3.1. Data Acquisition and Pre Processing
This consists of the data collection and signature image pre-processing stages. Our data was
collected from GPDS dataset because it is one of the well-known and widely used databases in
signature identification and verification applications and researches. GPDS dataset is a
standardized database of offline English signature images containing 4870 signature samples of
90 different writers. The pre-processing stage is responsible for improving the image quality, and
precedes the signature fragmentation phase. It makes extraction and identification of the
individual features much easier by improving the identification performance. Sometimes, some of
the original information is lost during the clean-up process of the background noise of the
digitized image. For the purpose of our study, a global threshold of Otsu’s algorithm was used in
this study to convert the selected dataset images into binary images of the signature. This binary
image serves as input to the next step.
3.2. Signature Image Windowing
In order to extract the writing patterns used frequently by an individual, we first implement
division (segmentation) on the signature to produce small sub-images (fragments). This division
is carried out by positioning small windows over the signature in a way that will exploit most of
the redundancy in the signature and produce signature fragments that allows for meaningful
comparison of these fragments. We have chosen to carry out this division in square windows of
nxn; this size should be large enough to contain ample information about the style of the author
and small enough to ensure a good identification performance. In this paper, the window size that
has been used is fixed at a value of 13 because a window size (13x13) can be applied to any input
image to get the optimum output. This adaptive window positioning method seeks to follow the
ink trace with the objective of achieving an optimal window positioning that is based on the
analysis of the skeleton of the handwritten signature image with respect to the drawing. This is
made possible by removing the pixels on the boundaries of the component without breaking it
apart, then placing the first window on the original component by finding the first image pixel
(the starting point of the written component) and scanning the pixel from top to bottom and left to
right as shown in Figure 2a. The second step is to copy the same placed window on the skeleton
image and define a four-flag direction (East, West, North and South) for each window, and set the
related flag if the skeleton exits from that particular side as shown in Figure 2b.
(a) (b)
Figure 2. (a) Skeleton trace. (b) Window positioning on a component
If the skeleton exits from the E, then the next window must be placed towards the right of the
current window (on the original component) as shown in Figure 3a, and then shift the window in
a vertical direction (up and down) to find its best position with respect to the text trace as in
Figure 3b, which shows how the window is placed to the right of an existing window and needs to
be moved in the vertical direction to find its final position. On the other hand, if the skeleton
exists from the N, then the next window must be placed on top of the current window and moved
horizontally (left or right) so as to be well placed over the text, In some cases where the skeleton

18
exists from more than one side, then the dividing process will treat each of the branches
separately.
(a) (b)
Figure 3. (a) Initial position of the next window. (b) Window sliding with respect to the
text trace (well placed window).
3.3. Windows Adjustment
Before comparing the similarities between the sub-images (fragments) and saving it on clusters,
the patterns must first be adjusted inside its window in order to prevent the disadvantages of
direct comparison which keep nxn pixel values (for a window size of n) thereby making the
comparisons susceptible to noise and distortion. Thus, an adjustment is done by repositioning the
image trace with respect to its window. We move the shape towards the upper-left corner of the
window as shown in Figure 4, such that only features that are independent of the window
positioning style are computed (in direct comparison). It is important to note that this window
adjustment does not include pattern rotation because the rotated versions are not considered since
they are not produced by the same gesture of the hand and thus should not be used or grouped in
the same class. Also we took into consideration the fact that same scale exists for handwritten
signature images within same sample, since the same writer cannot change the writing scale on
the data set. Thus, two sub-images cannot be compared using this feature because they are not
necessarily scale invariant due to the dataset standardization. We therefore assume that all images
are almost of the same image size and scanned in the same resolution.
Figure 4. Pattern adjustments inside its window
3.4. Features Extraction
Here, we extract the set of features (shape measures) from the patterns and represent the images
in a feature space. Since a typical data analysis problem involves many observations as well as a
good number of respective features, it is important to organize such data in a sensible way before
it can be presented and analysed by humans or machines. The aim of shape characterization is to
obtain shape measures to be used as features for classification in patterns.
For our study, we used “adaptive window positioning” technique for features extraction. This
technique, first proposed by (Siddiqi, Vincent, 2007), is more adapted to the ink trace and has the
flexibility to occupy a line position within a window. Since the images are offline, it is not
possible to follow the stroke trajectory that is followed by the writer. Nevertheless, this method to
some extent seeks to follow the ink trace with the objective of achieving an optimal window

19
positioning that is based on the analysis of the skeleton of the handwritten Signature image with
respect to the drawing.
3.5 Similarity Measurement
After representing the sub-images from a set of features, then it needed to do a similar measure
among all the windows which enables the comparison between two sub-images (fragments). The
two sub-images are compared with the following correlation similarity measurement:
Where, Nij is the number of pixels of the two sub-images X and Y, which they have the values i
and j respectively, at the corresponding pixel positions. This measure will be close to 1 if the two
compared sub-images are similar and in extreme case it will have a value equal to 1 indicating
that the two shapes are exactly the same.
3.5 Clustering to get the Code book
On this step is the clustering of the adjusted patterns of the sub-images will be done. The
objective is to group similar patterns in the same class, these classes would then correspond to the
frequency patterns occurring in writing. The number of elements per class, however, depends
upon the amount of text in the sample, so the sufficient number cannot be a fixed value. (See
figure 7 for more details).
4. DISCUSSION OF RESULT
In this section, the initial results of the proposed method have been applied and illustrated to
verify the validity of the earlier mentioned procedural methodology in section 3. The work is
implemented using Delphi programming language under Microsoft Windows operating system
environment and a demonstration of the performance is reported at the end of this section
4.1. Pre Processing Information
This is the first step carried out on the scanned signature image. In this step, pre-processing
mechanisms describe converting the image from a grey level image into binary image with
minimal consideration of the noise model [22]. From the image and to make it clearer and more
useful for the signature identification process. Figure 5 shows the before and after pre-processing
for a signature image.
.
(a) (b)
Figure 5. (a) And (b) are before and after the pre-processing respectively
(1)

20
4.2. Windowing
After identifying the components in an offline signature image, a division (windowing) procedure
was carried out on each component from top-bottom (vertical) and left-right (horizontal) onward
directions of the image trajectory, divided by (13x13) window size (see Figure 6a). Figure 6b
shows a magnified photo of the signature, which gives a clearer view of the windows division all
around the image following the trace trajectory with a well-positioned window indicating that no
overlap exists.
(a) (b)
Figure 6. (a) Offline signatures image division. (b) A closer look at the windows for the
signature part
4.3. Pattern Adjustment
Before moving to the features extraction stage, we adjusted pattern parts inside each window by
moving it to the upper left corner. This makes the calculation of the feature extraction processes
more accurate and easier. Figure 7a shows the patterns extracted from a handwritten signature
image of each window, while Figure 7b illustrates the patterns inside each window after applying
the necessary adjustment on each pattern.
(a) (b)
Figure 7. (a) Shows the extracted patterns. (b) Shows the extracted patterns after making the
adjustment

21
4.4. Features Extraction
This last three stages are considered the most important phases among all the procedural stages,
due to their impact on the effectiveness of the identification and verification process, the more
related features extraction leads to better categorized codebooks, as a consequence it results in a
better identification process. Figure 8 shows the result of the feature extraction after applying the
techniques mentioned in section 3. For instance, the row zero represents the first window, and
under the HH3 column, is the value 10 which represents the frequency of pattern that accrued in
the signature (please refer to section 3.6). The higher the value, the more it shows a specific
pattern with the original signature in the data set, which implies that this is a high similarity
between the test signature and the data set signature.
Figure 8. Shows the feature extraction
4.5. Similarity Measurement
Here, we classify the many features extracted from the input image into groups in terms of their
similarity attributes. A similarity function mentioned earlier in section 3 was coded in the
software to determine the range of values of the features variety and then classified into groups
according to a specific threshold magnitude (Figure 8).
4.6. Clustering Codebook
Clustering was used to classify the extracted features into classes based on our similarity function.
The number and length of classes varied depending on the authorship of the writer and the
signatures differences. The result of our experiment is illustrated in Figure 9, which shows the
clusters of a given input handwritten signature image, with a total number of 47 classes (c1 to
c47).

22
Figure 9. Sample Codebook for one Signature using our proposed Adaptive Window Positioning
Technique (A cluster of 47 classes).
4.7. Validation of Result
The validation of our process was tested with a GPDS dataset containing 4870 signature samples
from 90 different writers by comparing the robust features of the test signature with that of the
user’s signature using an appropriate classifier. GPDS is well known and one of the most widely
used database for offline signature identification and verification. In order to evaluate the
performance of the proposed method, we use three standard evaluation criteria: False Acceptance
Rate (FAR), False Rejection Rate (FRR), and Equal Error Rate (ERR).
Table 2. Shows the test result comparison of different methods with our proposed method on
1200 signature of 40 users.
Authors Methods FAR
(%)
FRR
(%)
EER
(%)
Proposed
method
(2014)
Adaptive
Window
Positioning
Techniques
8.68 6.12 7.40
Vargas et. al.,
2011
High Pressure
Polar
Distribution
14.66 10.01 12.33
Larkins et. al.
2009
Adaptive
Feature
Thresholding
10.96 8.16 9.66
Nguyen et.
al., 2009
Global Features
for Offline
Systems
17.25 17.26 17.25
Chen et. al.,
2006
Graph Matching 16.30 16.60 16.40
We used the FAR to account for all the skilled forgeries, FRR for only genuine signatures, and
ERR for verification. The ERR is computed from the average of the values derived from both
FAR and FRR and is a very good criterion for evaluating the accuracy of a method. The method

23
with the lowest ERR can be considered as the most accurate technique. Hence, results of our
experiment (Table II) reveal that adaptive window positioning technique proved to be the
efficient and most reliable method for accurate signature feature extraction for the identification
of offline handwritten signatures.
4. CONCLUSION
This paper proposed the use of the adaptive window positioning technique for offline handwritten
signature identification. It employed the division of signature images into 13x13 windows and
created some new cluster patterns for each window when classified into groups of similar
attributes. The results of our study show that the adaptive window positioning technique is a more
efficient and reliable method for accurate offline handwritten signature feature extraction. The
major contribution of this paper is that this technique can be employed by signature identification
and verification systems for the detection of offline handwritten signatures signed under duress or
emotional stress.
ACKNOWLEDGMENT
We wish to acknowledge the Faculty of Computing, Universiti Tecknologi Malaysia (UTM) for
their support to this research.
REFERENCES
[1] B. Miroslav, K. Petra, F. Tomislav, (2011) “Basic on-line handwritten signature features for personal
biometric authentication”, MIPRO, Proceedings of the 34th International Convention ,Opatija, May
2011, pp. 1458-1463.
[2] D. R. Shashikumar, K. B. Raja, R. K. Chhotaray, S. Pattanaik, (2010) “Biometric security system
based on signature verification using neural networks”, Computational Intelligence and Computing
Research (ICCIC), (Bangalore), pp 1-6.
[3] G. A. Khuwaja and M. S. Laghari, (2011)“Offline handwritten signature recognition”, World
Academy of Science, Engineering and Technology, vol. 59, pp. 1300-1303.
[4] H. B. Kekre, V. A. Bharadi, (2010) “Gabor filter based feature vector for dynamic signature
recognition”, International Journal of Computer Application, vol. 2 (3), May, pp 74-80.
[5] Y. M. Al-Omari, S. H. S. Abdullah, and K. Omar, (2011) “State-of-the-art in offline signature
verification system”, International Conference on Pattern Analysis and Intelligent Robotics, June 2011,
Putrajaya, (Malaysia), pp. 59-64.
[6] S. Arora, D. Bhattacharjee, M. Nasipuri , L. Malik , M. Kundu and D. K. Basu, (2010) “Performance
comparison of SVM and ANN for handwritten Devnagari character recognition”, International Journal
of Computer Science Issues, vol. 7 (3), May, pp. 1-10.
[7] E. Alattas, and S. Meshoul, (2011)“An effective feature selection method for on-line signature based
authentication”, Eighth International Conference on Fuzzy Systems and Knowledge Discovery
(FSKD), (Shanghai), vol. 3, July, pp.1431-1436.
[8] S. A. Daramola, T. S. Ibiyemi , (2010)“Offline signature recognition using Hidden Markov Model
(HMM)”, International Journal of Computer Applications, vol. 10 (2), November, pp. 17-22.
[9] S. K. Shrivastava, S. Gharde, (2010) “Support vector machine for handwritten Devanagari numeral
recognition” International Journal of Computer Applications (0975 – 8887), vol. 7 (11), October.
[10] D. Samuel, and I.Samuel , (2010) “Novel feature extraction technique for off-line signature
verification system”, International Journal of Engineering Science and Technology ,vol. 2 (7), pp.
3137-3143.
[11] S. K. Shrivastava, and S. S. God, (2010) “Support vector machine for handwritten Devanagari numeral
recognition” International Journal of Computer Applications (0975 – 8887), vol. 7 (11), October.
[12] B. Singh, A. Mittal, and D. Ghosh, (2011)“An evaluation of different feature extractors and classifiers
for offline handwritten Devnagari character recognition”, Journal of Pattern Recognition Research, pp.
269-277.

24
[13] D. R. Kisku, P. Gupta, and J. K. Sing, (2010)“Offline signature identification by fusion of multiple
classifiers using statistical learning theory”, International Journal of Security and its Applications, vol.
4 (3), July, pp. 35-45.
[14] Siddiqi and Vincent, (2010)"Text independent writer recognition using redundant writing patterns with
contour-based orientation and curvature features", Pattern Recognition, vol. 43, pp. 3853–3865.
[15] Vu Nguyen, M. Blumenstein, V. Muthukkumarasamy, and G. Leedham, (2007)“Off line signature
verification using enhanced modified direction features in conjunction with neural classifiers and
support vector machine”, Ninth International Conference on Document Analysis and Recognition , Vol
2, pp. 734-738.
[16] O. Mirzaei, H. Irani , H. R. Pourreza , (2011)“Offline signature recognition using modular neural
networks with fuzzy response integration”, International Conference on Network and Electronics
Engineering IPCSIT, Vol. 11, (Singapore), pp. 53-59.
[17] M. K. Kalera, S. Sriharly, A. Xu, (2004) "Offline signature verification and identification using
distance statistics", International Journal of Pattern Recognition and Artificial Intelligence, vol. 18 (7),
pp. 1339-1360.
[18] S. M. Odeh and M. Khalil, (2011) “Off-line signature verification and recognition: Neural Network
Approach”, International Conference On Innovations In Intelligent Systems And Applications
(INISTA), pp. 34-38.
[19] M. H. Sigari, M. R. Pourshahabi, and H. R. Prize, (2011)“Off-line handwritten signature identification
and verification using multi-resolution Gabor wavelet”, IJBB, vol. 5, pp.1-15.
[20] M.S. Shirdhonkar and M. Corker, (2011)“Off-line handwritten signature identification using rotated
complex wavelet filters”, International Journal of Computer Science Issues, Vol. 8 (1), January , pp.
478-482.
[21] M. R. Pourshahabi, M. H. Sigari, and H. R. Prize, (2009)"Offline handwritten signature identification
and verification using contourlet transform", International Conference on Soft Computing and Pattern
Recognition, Malacca,(Malaysia), December, pp. 670-673.
[22] M. Rama Bai, (2013)”Composite Texture Shape Classification Based on Morphological Skeleton and
Regional Moments “Signal & Image Processing: An International Journal (SIPIJ), India, Vol.4, No.3,
June, pp. 4313.
AUTHORS
Ghazali Sulong received his BSc degree in statistic from National University of
Malaysia, In 1979, and MSc and PhD in computing from University of Wales ,
Cardiff , United Kingdom , in 1982 and 1989, respectively. He is currently a
professor at the Faculty of Computing , Universiti Teknologi Malaysia . His research
interest includes Biometric – Fingerprint Identification , face recognition , iris
verification, ear recognition, handwriting Recognition, and writer identification ; object
recognition ; medical image segmentation,Enhancement and restoration; human activities recognition; data
hiding - digital watermarking And Steganography ; image encryption; image compression ; image fusion ;
image mining ; Digital image forensics; object detection, segmentation and tracking.
Anwar Yaya Ebrahim received her B.Sc. degree from Babylon University, Iraq in
2000. And her M.Sc. degree from MGM College Dr. Babasaheb Ambedkar
Marathwada University,India in 2009, currently she is PHD.Student in Universiti
Technology Malaysia (UTM). Her Research interests include signatures
identification and verification , features recognition, Image analysis and classification.
Muhammad Jehanzeb received his B.Sc. degree from Arid Agriculture University,
Pakistan in 2005, his M.S.degree from Iqra University, Pakistan, in 2007. He earned his
Ph.D. at Universiti Teknologi Malaysia (UTM) in 2013. Presently, he is working as
Post-Doctoral Fellow at Universiti Teknologi Malaysia (UTM). His research interests
include document analysis and recognition, pattern recognition, image analysis and
classification.

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