Download Signature Identification and Recognition using Elman Neural Network

European Journal of Scientific Research
ISSN 1450-216X / 1450-202X Vol. 131 No 2 April, 2015, pp.175 - 181
http://www.europeanjournalofscientificresearch.com
Signature Identification and Recognition using
Elman Neural Network
Hind Rostom Mohammed
Assistant Professor / Computer Science Department
Faculty of Mathematics & Computer Science, Kufa University, Iraq
E-mail: [email protected]
Elaf Jabbar Abdul Razzaq Al-Taee
Assistant Lecturer/ Law Department, Faculty of Law
Kufa University, Iraq
E-mail: [email protected]
Abstract
The problem of signature recognition, which is a special case of image analysis,
describes how to extracts the features efficiently and verifies the signature perfectly.
Human signature can be handling as the image and can be Identified by using Elman neural
network. This paper deals with the signature using global features extraction and Elman
neural network in which the human signature is captured In image acquisition stage,
scanner was used as image acquisition device. In image processing, many techniques are
used, through which the signature is extracted from the original image, filtered from noises,
and image improvements that prepare the signature to the feature extraction stage. Feature
extraction stage which global features is implemented and ending with vector of values for
feature extraction method that describes the signature image features. Each feature vector is
fed Elman neural network which, that will characterize the signature to whom it belongs.
Data base contain 500 signatures for 100 person each person have 5 signatures with
different orientation, size, deviation, etc. In an Elman network, the weights from the hidden
layer to the context layer are set to one and are fixed because the values of the context
neurons have to be copied exactly. The initial output weights of the context neurons are
equal to half the output range of the other neurons in the network. The Elman network can
be trained with gradient descent back propagation and optimization methods. The accuracy
of proposed method was is highly promised result and dependable.
Keywords: Signature, Neural Networks, Signature Identification and Recognition, Elman
neural network.
1. Introduction
Signature authentication technology uses the dynamic analysis of a signature to authenticate a person.
The technology is based on measuring speed, pressure and angle used by the person when a signature
is produced. This technology uses the individual's handwritten signature as a basis for authentication of
entities and data. An electronic drawing tablet and stylus are used to record the direction, speed and
coordinates of a handwritten signature [1].
Signature Identification and Recognition using Elman Neural Network
176
Signature recognition is the process of verifying the writer’s identity by checking the signature
against samples kept in the database. The result of this process is usually between 0 and 1 which
represents a fit ratio (1 for match and 0 for mismatch). Signature recognition is used most often to
describe the ability of a computer to translate human writing into text. This may take place in one of
two ways either by scanning of written text (off-line method) or by writing directly on to a peripheral
input device. The first of these recognition techniques, known as Optical Character Recognition (OCR)
is the most successful in the main stream. Most scanning suites offer some form of OCR, allowing user
to scan handwritten documents and have them translated into basic text documents. OCR is also used
by some archivist as a method of converting massive quantities of handwritten historical documents
into searchable, easily-accessible digital forms. As signature is the primary mechanism both for
authentication and authorization in legal transactions, the need for efficient auto-mated solutions for
signature verification has increased [2].
Our approach is to verify an entered signature with the help an Global properties signature,
which is obtained from the set of, collected signatures, we have followed the concept of used Mean
square error performance function (MSE) and Mean absolute error performance function (MAE) to
find out the amount of divergence in between them using Elman neural network and which is defined
as discrimination and signature and identify the person concerned.
The rest of the paper is organized as follows: Section 2 gives a brief outline of the 2. Feature
extraction and Elman neural network, section 3 describes the process of building results and
experiments and shows the experimental results obtained using our method. section 4 gives concluding
remarks.
2. Related Work
Baltzakis et al. (2001) the proposed system is based on global, grid and texture features. Karouni et al.
(2011) present a method for Offline Verification of signatures using a set of simple shape based
geometric features. Srinivas et al. (2012) Objective of this work is to recognize the hand written digits
represented in black-and-white rectangular pixel displays using Elman neural network (ENN).Bhatia et
al.(2013) off-line signature recognition & verification using neural network is proposed, where the
signature is captured and presented to the user in an image format.
3. Feature Extraction and Elman Neural Network
Feature extraction is a general term for methods of constructing combinations of the variables to get
around these problems while still describing the data with sufficient accuracy [3]
There are different types of features such as global, grid, texture, and local feature. Global
features provide information about specific cases concerning the structure of the signature. Many
object recognition systems use global features that describe an entire image. Most shape and texture
descriptors fall into this category. Such features are attractive because they produce very compact
representations of images, where each image corresponds to a point in a high dimensional feature
space. As a result, any standard classifier can be used [4].
There are many Global features such as [3.4]:
1. Area of Image
The area of image is calculated by summing all the black pixels in the image.
2. Signature Height
This feature is calculated by compute signature height and width.
Height equal the difference between the highest and lower black pixels for image.
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Hind Rostom Mohammed and Elaf Jabbar Abdul Razzaq Al-Taee
Width equal the difference between the beginning and end black pixels for horizontal
coordinate image.
3. Maximum Horizontal Projection
The maximum horizontal projection is calculated by summing all pixels along each row and extracts
the maximum projection between them.
4. Maximum Vertical Projection
The maximum vertical projection is calculated by summing all pixels along each column and extracts
the maximum projection between them.
5. Dimension
For more details, we study the dimension of the image which represent the degree of complexity of this
image. It is calculated from the following relationship:
Dimension =Area /(n*m) where (n, m) = size of image
The Elman network was introduced by Elman in 1990. In this network a set of context units are
introduced_ which are extra input units whose activation values are fed back from the hidden units.
Thus the network is very similar to the Jordan network_ except that
1) The hidden units instead of the output units are fed back
2) The extra input units have no self-connections [5].
Elman neural network is feed forward network with an input layer, a hidden layer, an output
layer and a special layer called context layer. The output of each hidden neuron is copied into a specific
neuron in the context layer. The value of the context neuron is used as an extra input signal for all the
neurons in the hidden layer one time step later. In an Elman network, the weights from the hidden layer
to the context layer are set to one and are fixed because the values of the context neurons have to be
copied exactly [6].
The key element of this paradigm is the structure of the information processing system. It is
composed of a large number of highly interconnected processing elements (neurons) working together
to solve specific problems. ANNs, its just like people, learn by example. An ANN is designed for a
specific application, such as a data classification, through a learning process. Learning in biological
systems involves adjustments to the synaptic connections that exist between the neurons. This is true of
ANNs as well [5].
4. Results and Experiments
In this section a detailed experimental Signature Identification and Recognition has been presented. We
have used Data base contain 500 signatures for 100 person each person have 5 signatures with different
orientation, size, deviation, etc. The flowchart for system show in figure (1) and figure (2) show the
sample of signature images while figure (3) show the sample of signature images testing for one person
in this paper.
The next stage is feature extraction concerns finding for Signature images. To be able to
recognize Signature automatically. In feature extraction, we generally seek invariance properties so
that the extraction process does not vary according to chosen (or specified) conditions. Global features
are sensitive to clutter and occlusion. As a result it is either assumed that an image only contains a
single object, or that a good segmentation of the object from the background is available [7].Signatures
for this research work are taken from kufa university students.
Signature Identification and Recognition using Elman Neural Network
Figure 1: Flowchart for System
Start
Read the image file
Converting image file from Color Image to Gray
Image
Gray Image Enhancement
Converting Gray Image to Binary Image
Global Feature Extraction
Apply the Elman Neural Network
Calculate MSE
Calculate MAE
End
Figure 2: The Sample of Gray Scale Images
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Hind Rostom Mohammed and Elaf Jabbar Abdul Razzaq Al-Taee
Figure 3: Sample of Signature Images Testing for one Person
Figure 4 Showed Global Features for one Person each Person have 5 Signatures
Figure 4: Signatures for Person 1
Figure 8 showed an Elman Structure neural network [6]. This network has six input neurons,
five hidden neurons and six output neurons. There are also five context neurons. The context neurons
receive input from the hidden layers and also pass their output to the hidden layers. The context layers
always store the output from the hidden layer and relay this information in the next iteration. This
allows them to form a sort of short term memory.
Figure 8: Elman Structure Neural Network
Signature Identification and Recognition using Elman Neural Network
180
Structural neural network and training: In this research was used Elman neural network and that the
results of accurate and efficient in signature verification and distinctiveness. Based network in its work on
the values of the Global features of the signature. Where the network receives mono a matrix include 6
values are the results of the Global features of the signature. To illustrate the network layers separately:
1. Input layer: the first layer of Elman network, which includes mono matrix and the number
of nodes in this layer (6 nodes) the number of values of the results of the Global features
of the signature.
2. Hidden layer: the number of nodes (5 nodes) and the return of my time (context layer) and
that gives feedback without weight.
3. Output layer: consists of (6 nodes) according to data target (the results of the Global
features of the signature).
After the stage of network construction phase begins training the network where they are taking
the values of the results of the Global features of the input of the network where the network is trained
on error rate (0.001) and the number of cycles of (1000) cycle to reach the desired goal (the results of
the Global features of the signature) concept of used Mean square error performance function(MSE)
and Mean absolute error performance function(MAE) to find out the amount of divergence in between
them using Elman neural network and which is defined as discrimination and signature and identify the
person concerned.
Figure 9: Mean Square Error Performance Function (MSE) and Mean Absolute Error Performance Function
(MAE) for Person 1.
Figure 12: Elman Neural Network effects for person (1) contain 5 patterns
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Hind Rostom Mohammed and Elaf Jabbar Abdul Razzaq Al-Taee
Conclusion
This paper present comparative analysis of performance and accuracy of elman neural network.
Applied the Elman neural network on Data base contain (500) signatures for (100) person each person
have (5) signatures. The results obtained from this research work shows the following conclusion:1. We conclude that all five models of the signatures of one person to be the values of MSE and
MAE very close. This leads to distinguish this signature to this person.
2. Calculate mse is a network performance function. It measures the network's performance
according to the mean of squared errors.
3. Calculate mae is a network performance function. It measures network performance as the mean
of absolute errors.
4. Accuracy going to obtain 100% has been achieved In future research, a larger signature database
will be collected, including multilingual signatures, to investigate the techniques proposed in this
paper.
With all the results shown above we can conclude that this new approach performs better.
Acknowledgment
I would like to thank all those people who made this search possible and an unforgettable experience
for me.
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