I J C S S T, Vol. 5, No. 1, January-June 2012, pp. 35-38© International Science Press, ISSN: 0974-3898

RECOGNITION OF MARK AUTHENTICATION USINGNEURAL NETWORKS

1S. Muthukumaran, 2Vigneshwara Raj, D. and 3S. Antony Jones1,2,3Asst.Prof., PG and Research Department of Computer Science

St. Joseph’s College of Arts and Science (Autonomous)-Cuddalore-01E-mails: 1muthu.svk06@gmail.com, 2Vikyraaj@gmail.com, 3joelsar1986@gmail.com

Abstract: Authentication on the neural network is the problem which is analyzed in this paper. The effort has been extendedin making a computer recognize both typed and hand written characters automatically. The focus has been Endeavour onalphabets of English language. Methods currently used for character recognition for these languages are mainly those whoinvolved pattern matching using image processing techniques .The purpose of this paper is to give developers with littleknowledge of authentication in neural network and it is used to determine whether the pen based signature matches theexisting digital signature. The sobeledge detection algorithm is used to compare the image and conclude whether the signaturematches with the value of digital image. The backpropagation and perceptron technique are used for recognizing characterand number.

Keywords: Authentication, BPN, slobleedge, perceptron, neuron

1. INTRODUCTION

Artificial Neural Network (ANN) is an information-processing paradigm that is inspired by the waybiological nervous systems, such as the brain, processinformation. The key element of this paradigm is thenovel structure of the information processing system.It is composed of a large number of highlyinterconnected processing elements (neurones)working in unison to solve specific problems. It isconfigured for a specific application, such as patternrecognition or data classification, through a learningprocess. Learning in biological systems involvesadjustments to the synaptic connections that existbetween the neurones. Thus it becomes important toanalyze authentication between pen based digitalsignature and pattern matching signature. In this paper,we have used the two important technique namelybackpropagation and perceptron for recognizingcharacter and number. Finally slobel edge detectionalgorithm with Laplace formula is used find the imageand value of the signature.

2. BACK-PROPAGATION IN NEURALNETWORK

The application of the generalized delta rule thusinvolves two phases: During the first phase the input xis presented and propagated forward through the

network to compute the output values zero for eachoutput unit. This output is compared with its desiredvalue resulting in an error signal ä zero for each outputunit [1]. The second phase involves a backward passthrough the network during which the error signal ispassed to each unit in the network and appropriateweight changes are calculated.

2.1. Character and Number Recognition using BPNTechnique

New characters can be added to the database bydrawing the character in the blue box on the left side,selecting which character it will map onto and thenpressing Add character. When done entering all newcharacters, pressing Learn characters will make theprogram learn the characters in the database usingbackpropagation [2]. The program does this by firstinitializing all weights and biases with random valuesbetween 0.0 and 1.0.

Units are connected to one another. Connectionscorrespond to the edges of the underlying directedgraph. There is a real number associated with eachconnection, which is called the weight of theconnection. We denote by Wij the weight of theconnection from unit ui to unit uj. It is then convenientto represent the pattern of connectivity in the networkby a weight matrix W whose elements are the weights

36 S. Muthukumaran, Vigneshwara Raj D. and S. Antony Jones

Wij. Two types of connection are usually distinguished:excitatory and inhibitory. A positive weight representsan excitatory connection whereas a negative weightrepresents an inhibitory connection. The pattern ofconnectivity characterizes the architecture of thenetwork.

3.1. Character and Number Recognition usingPerceptron Technique

This class is responsible for recognition of a character.It is used to determine given character by comparingwith the knowledge base which is already given asinput.

3.2. Number Scaling

Since a neural network only receives data in form ofinput stimuli it cannot recognize different sizes of acharacter without having to learn all possible sizes [4].By letting the program scale characters this problemwould be reduced to a minimum.

3.3. Number Centering

Another problem appears when the user drawscharacters with a different alignment i.e. drawing a leftaligned I when all I’s that have been learned arecentered. Letting the program center all charactersbefore they are stored in the database and beforeinterpreting them can easily solve this.

Figure 2.1: Recognition of Single Character and Number

Advantage of Back-Propagation

The advantage of this model is less number of iterationand better performance compare with standard back-propagation model. To evaluate this algorithm, wesimulated some cases of classification data anddifferent setting of network factors (e.g. hidden layernumber and nodes, number of classification andIteration).

3. PERCEPTRON IN NEURAL NETWORK

The Perceptron is a single layer neural network whoseweights and biases could be trained to produce a correcttarget vector when presented with the correspondinginput vector. The training technique used is called theperceptron-learning rule [3]. The perceptron generatedgreat interest due to its ability to generalize from itstraining vectors and work with randomly distributedconnections. Perceptron are especially suited for simpleproblems in pattern classification. Our perceptronnetwork consists of a single neuron connected to twoinputs through a set of 2 weights, with an additionalbias input. The perceptron calculates its output usingthe following equation: P * W + b > 0 Where P is theinput vector presented to the network, W is the vectorof weights and b is the bias.

Figure 3: Perceptron Network Figure 3.3.1: Character Recognition using Perceptron

Figure 3.3: Number Recognition using Perceptron

Recognition of Mark Authentication using Neural Networks 37

4. SLOBAL EDGE

This is used to compare the images and concludewhether the signature matches or not. Based on thisone-dimensional analysis, the theory can be carriedover to two-dimensions as long as there is an accurateapproximation to calculate the derivative of a two-dimensional image. The Sobal operator performs a 2-D spatial gradient measurement on an image [5].Typically it is used to find the approximate absolutegradient magnitude at each point in an input grayscaleimage. The Sobal edge detector uses a pair of 3x3convolution masks, one estimating the gradient in thex-direction (columns) and the other estimating thegradient in the y-direction (rows).

As a result, the mask is slid over the image,manipulating a square of pixels at a time. The actualSobal masks are shown below:

The problem of overlapping structures is overcome bythis algorithm because it takes into consideration globaledge information.

4.2. Sobal Curve Detection

The mask is slid over an area of the input image,changes that pixel’s value and then shifts one pixel tothe right and continues to the right until it reaches theend of a row. It then starts at the beginning of the nextrow.

The example below shows the mask being slid overthe top left portion of the input image represented bythe green outline [7]. The formula shows how aparticular pixel in the output image would be calculated.

The center of the mask is placed over the pixelyou are manipulating in the image. And the I & J valuesare used to move the file pointer so you can multiply.

4.3. LAPLACE for Image in Pixel

The 5x5 Laplacian used is a convoluted mask toapproximate the second derivative, unlike the Sobalmethod which approximates the gradient. And insteadof 2 3x3 Sobal masks, one for the x and y direction,Laplace uses 1 5x5 mask for the 2nd derivative in boththe x and y directions [8].

However, because these masks are approximatinga second derivative measurement on the image, theyare very sensitive to noise; The Laplace mask and codeare shown below:

Figure 4: Slobal Edge Value Deduction

4.1. Edge Detection

An edge detection algorithm was developed based onthe assumption that the LV border can be defined asthe maximum, normalized, closed-line integral of aclosed curve in a vector field derived by imagedifferentiation. It is further assumed that the closedcurve can be described by a Fourier expansion with alimited number of harmonics. Regions of interest(ROIs) generated by this algorithm were compared withROIs generated by an algorithm based on acombination of threshold and second-order derivatives.

This algorithm delineates the left ventricle andgives results more closely related to ROIs generatedmanually than the algorithm combining threshold andthe second-order derivative [6]. Our algorithm can alsohandle the problem of overlapping structures, asdemonstrated in phantom simulations.

The concept of a maximum, normalized closed-lineintegral will improve the delineation of the LV in anequilibrium radio nuclide angio-cardiography study.

Figure 4.3: Slobal Edge Detection using Laplace

5. RESULTS AND DISCUSSIONS

In this method Different styles of fonts and formatsfor each character and number are created and storedin a file. When the user gives an input, the system willcompare the input with the standard formats stored in

38 S. Muthukumaran, Vigneshwara Raj D. and S. Antony Jones

the file and produce the appropriate result. Perceptronmethod is said to be less dynamic since the weightmatrix cannot be changed. Perceptron is mostlyconsidering as a non-training mode. So to make it moredynamic, we go in for back propagation method. Thismethod is referred to as the training mode since it trainsthe weight matrix to be changed to correct any error incase it occurs.

correct the value of Mean Square Error in whichappropriate character is recognized. Perceptron, whichis used for weighted matrix could not be changed.Character Recognition is done when the character isdrawn. Number Recognition can be done by perceptronand particular number will be stored in m*n matrix. Inthis case binary input is converted into bipolar valueNeural Network.

Mark authentication can be done by using slobaledge detection algorithm. This method especially dealswith signature matching. The edges will be calculatedfor the user input of the signature stored. It comparesthe input pattern, which the user has given with thestandard pattern given.

References

[1] Russell D. Reed, “Neural Smithing: Supervised Learningin Feed forward Artificial Neural Networks”.

[2] Ripley, B. D., “Pattern Recognition and Neural Networks”.Cambridge University Press. 1996.

[3] Simon Haykin, Neural Networks: A ComprehensiveFoundation (2nd Edition).

[4] Patterson, D, “Artificial Neural Networks.” Singapore:Prentice Hall. (1996).

[5] Jernigan, M. E. and Wardell, R. W “Does the Eye ContainOptimal Edge Detection Mechanism”, No. 6, 1981, 441-444. BibRef 8106.

[6] Sugiyama, T.[Takahiro], Abe, K.[Keiichi], “Edge DetectionMethod Based on Edge” ICPR00(Vol III: 656-659).

[7] Lancaster, I. T., Elliman, D. G., A Comparison of TwoAlgorithms for Segmentation Using Edge DetectionTechniques, PRL(11), 1990, pp. 175-180.

[8] Berzins, V. “Accuracy of Laplacian Edge Detectors”,CVGIP (27), No. 2, August 1984, pp. 195-210.

Figure 5.1: Mark Authentication using Slobal Edge

This class is responsible for verifying the signature.It is used to determine whether the signature matchesthe existing signature, which is already given as input.In this module comparison is mainly done for edgesand then the result is produced.

6. CONCLUSION

Character Recognition techniques associated with asymbolic identity with the image of character. Thecharacter Recognition method is based on perceptronand Back propagation technique is used to train thenode to correct the error if occurs. The characterrecognition is from the Mean Square Error<=0.01.Ifthe output value does not satisfy the condition erroroccurs otherwise weighted Matrix will be changed and