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(IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 Off-line Handwritten Signature Recognition Using Wavelet Neural Network Mayada Tarek1 Taher Hamza Elsayed Radwan Computer Science Department, Computer Science Department, Computer Science Department, Faculty of Computers and Information Faculty of Computers and Information Faculty of Computers and Information Sciences, Sciences, Sciences, Mansoura, Egypt Mansoura, Egypt Mansoura, Egypt AbstractــــAutomatic signature verification is a well- in conjunction with a simple Euclidean distance established and an active area for research with numerous classifier; proposing a system for off-line signature applications such as bank check verification, ATM access, verification consists of four subsystems based on etc. Most off-Line signature verification systems depend geometric features, moment representations, envelope on pixels intensity in feature extraction process which is characteristics and wavelet features; applying wavelet sensitive to noise and any scale or rotation process on on signature verification [2,3,4,5]. signature image. This paper proposes an off-line handwritten signature recognition system using Discrete Although these methods achieved a good results, they Wavelet Transform as feature extraction technique to still suffer from the exchangeability of signature extract wavelet energy values from signature image rotation and the distinguish-ability of person signature without any dependency of image pixels intensity. Since size. Most of these feature extraction methods depend Discrete Wavelet Transform suffers from down-sample on signature shape or pixels intensity in specific region process, Wavelet Neural Network is used as a classifier to of signature. However, pixels' intensity are sensitive to solve this problem. A comparative study will be illustrated noise and also the signature shape may vary according between the proposed combination system and pervious to translation, rotation and scale variations of signature off-line handwritten signature recognition systems. image [6]. Conclusions will be appeared and future work is proposed. Two types of feature can be extracted from signature image; first, global features which are extracted from Keywords-Discrete Wavelet Transform (DWT); Wavelet the whole signature, including block codes [7]; second, Energy; Wavelet Neural Network (WNN); Off-line local features which are calculated to describe the Handwritten Signature. geometrical and topological characteristics of local segments [8]. Because of the absence of dynamic I. INTRODUCTION information in offline verification system, global features extraction are most appropriate [9]. One of the In the field of personal identification, two types of most appropriate global features extraction techniques is wavelet transform, since it extracts time-frequency biometrics means can be considered; first, physiological wavelet coefficients from the signature image [8]. biometrics, which involves data derived from the direct Wavelet Transform is especially suitable for processing measurement of some part of the human body; for- an off-line signature image where most details could be example fingerprint-, face-, palm print-, retina-based hardly represented by functions, but could be matched verification. Second, behavioural biometrics, which by the various versions of the mother wavelet with involves data derived from an action taken by a person, various translations and dilations [10]. Also, wavelet or indirectly measures characteristics of the human transform is invariant to translation, rotation and scale of the image. Because of the advantage of wavelet body; for-example: speech-, keystroke dynamics and transform, this paper uses it in feature extraction stage. signature-based verification [1]. Since one of problems that face wavelet is the huge size In the last few decades, researchers have made great of its coefficients, statistical model can be introduced to efforts on off-line signature verification [1] for- represent them. This paper uses wavelet energy as example; using the statistics of high grey-level pixels to statistical model to represent all wavelet coefficients in identify pseudo-dynamic characteristics of signatures; efficient way. Another problem is down-sample process developing technique based on global and grid features which can lose some important extracted feature from signature image[11]. This paper proposes a Wavelet 1 Neural Network (WNN) technique for off-line signature Corresponding Author Mail: mayaatarek@yahoo.com recognition to overcome the disadvantages of Discrete Tel : 020108631688 Wavelet Transform (DWT) down-sample process. 1 13 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 WNN takes full advantages of the partial-resolution image. Dynamic systems use on-line acquisition devices characteristic of the wavelet transform and the nonlinear that generate electronic signals representative of the mapping behaviour of Artificial Neural Networks signature during the writing process [1]. (ANN) [15]. It is well known that no two genuine signatures of a This paper proposes a combination model between person are precisely the same and some signature DWT and WNN techniques for off-line handwritten experts note that if two signatures written on paper were signature recognition system. DWT technique will same, then they could be considered as forgery by analysis signature image to extract wavelet detail tracing .Unfortunately, off-line signature verification is coefficients. To reduce the huge number of these a difficult discrimination problem because of dynamic coefficients with the same accuracy, a statistical model information regarding the signing velocity, pressure and is represented by wavelet energy. Because of the stroke order are not available also an off-line problem of down sample, WNN technique will be used handwritten signature is depend for instance on , the as a suitable classifier technique to overcome this angle at which people sign may be different due to problem. Also, a modified back-propagation technique seating position or due to support taken by hand on the is used in learning WNN. A testing stage examines the writing surface and all this information can’t be extract unseen signature. Moreover, a comparative study will from static image[12]. be illustrated between the proposed combination system and pervious off-line handwritten signature recognition systems. Conclusions will be appeared and future work B. Wavelet Transform : is suggested. Wavelet Transform (WT) [13] is become a powerful The rest of this paper organized as; in Section 2, alternative analysis tool to Fourier methods in many Handwritten signature, wavelet transform (WT), signal processing applications. The main advantages of Wavelet Neural Network (WNN) are mentioned. wavelets is that they have a varying window size, being Methodology and applications using a combination wide for slow frequencies and narrow for the fast ones, between DWT and WNN techniques is described in thus leading to an optimal time-frequency resolution in Section 3. Section4, consists of the result of the all the frequency ranges. Furthermore, owing to the fact proposed combination system and a comparative study that windows are adapted to the transients of each scale, between three strategies (signature image pixels wavelets lack the requirement of stationary. There are intensity value as input to ANN , signature wavelet two types of Wavelet Transform; Continous Wavelet energy values as input to ANN and signature wavelet Transform(CWT), Discrete Wavelet Transform (DWT). energy values as input to WNN). Finally section 5 concludes the paper. The Continuous Wavelet Transform [14] of a 1-D signal x(t) is defined as in equation (1): 1 t−b II. PRELIMINARIES ����(a,b) (t)= ����(����) ���� ( ) dt (1) √|����| ���� a A. Handwritten Signature Where ψ(t) is the mother wavelet or the basis function which, in a form analogous to sins and cosines in Handwritten signatures are widely accepted as a means Fourier analysis. All the wavelet functions used in the of document authentication, authorization and personal transformation are derived from the mother wavelet verification. For legality most documents like bank through translation (shifting) b and scaling (dilation or cheques, travel passports and academic certificates need compression) a. to have authorized handwritten signatures. In modern society where fraud is rampant, there is the need for an The Discrete Wavelet Transform [14], which is based automatic Handwritten Signature Verification system on sub-band coding is found to yield a fast computation (HSV) [6]. Dependency on automation is due to the of wavelet transform. It is easy to implement and difficulty faced in visual assessment for different types reduces the computation time and resources required. and different sizes of signatures. Simple, cursive, graphical and not a connected curve pattern are some of In CWT, the signals are analyzed using a set of basis the different types of signatures and machines are far functions which relate to each other by simple scaling superior when it comes to processing speed and and translation. In the case of DWT, a time-scale management of large data sets with consistency [12]. representation of the digital signal is obtained using digital filtering techniques. The signal to be analyzed is passed through filters with different cut off frequencies Automatic HSV systems are classified into two types: at different scales[14]. offline HSV and online HSV: static or off-line system and dynamic or on-line system .Static off-line system In DWT, the extension to 2-D is usually performed by gain data after writing process has been completed .In using a product of 1-D filters. The transform is computed by applying a filter bank as shown in this case the signature is represented as a grey level Figure 1. L and H to denote the 1-D low pass and high 2 14 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 pass filter, respectively. The rows and columns of image neuron parameters .The output of WNN is therefore a are processed separately and down sampled by a factor linear combination of several multidimensional of 2 in each direction which may cause losing important wavelets [15]. feature. Resulting in one low pass image LL and three detail images HL, LH, and HH. Figure 2a shows the one-level decomposition of Figure 1in the spatial domain. The LH channel contains image information of low horizontal frequency and high vertical frequency, the HL channel contains high horizontal frequency and low vertical frequency, and the HH channel contains high horizontal and high vertical frequencies. Three- level frequency decomposition is shown in Figure 2b. Note that in multi-scale wavelet decomposition only the LL sub-band is successively decomposed [13]. Figure 3 : The structure of the Wavelet Neural Network In this WNN model, the hidden neurons have wavelet activation functions ψ and have two parameter at,bt which represent dilation and translation parameter of wavelet function and V is the weight connecting the input layer and hidden layer and U is the weight connecting the hidden layer and output layer. Figure 1: A one-level wavelet analysis filter bank. Let Xn ={ xi },i=1,......,L and n=1,......N be the WNN input to no. n sample ; Yn ={ yk },k=1,......,S represents the output of WNN ; D={ dk },k=1,......,S represents the expected output ; Vij represents the connection weight between no. i node (input layer) and . j node (hidden layer) ; Ujk represents the connection weight between no. j node (hidden layer) and k node (output layer) . Where N is the number of Sample ; S is the number of output node ; L is the number of input node ; M is the number of hidden layer. Figure 2 : Wavelet frequency decomposition. III. WAVELET NEURAL NETWORK FOR OFF-LINE HANDWRITTEN SIGNATURE C. Wavelet Neural Network : RECOGNITION WNN is a combination technique between neural According to the fact that there aren’t two genuine network and wavelet decomposition .The advantages of signatures of one person are precisely the same, many the WNN are a high-speed learning and a good efforts have been done in order to comprehend the convergence to the global minimum [15].The reason for delicate nuances of person signatures [12]. Especially the application of WNN in case of such a problem as off-line signature recognition needs more effort because classification is that the feature extraction and of the absence of dynamic information that can’t be representation properties of the wavelet transform are extracted from static image [12]. Also, the problems of merged into the structure of the ANN to further extend translation, rotation and scale variation of signature the ability to approximate complicated patterns [16]. image are still found when dealing with signature image pixels’ intensity [6]. The WNN can be considered an expanded perceptron [17]. The WNN is designed as a three-layer structure This paper presents an implementation for off-line with an input layer, a wavelet layer, and an output layer. handwritten signature recognition system using DWT The topological structure of the WNN is illustrated in technique in feature extraction phase and WNN in Figure 3. classification phase to overcome all the above problems with off-line handwritten signature recognition system. In WNN, both the position and dilation of the wavelets DWT technique depends on analyzing all signature as well as the weights are optimized. The basic neuron shapes (continuous case) instead of analyzing the pixels of a WNN is a multidimensional wavelet in which the intensity or segmentation part of signature (discrete dilation and translation coefficients are considered as case). Because of the problem of down-sample caused 3 15 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 by DWT technique, WNN technique will be used in According to the fact that there aren’t two genuine classification stage to overcome this problem. signatures of one person are precisely the same, the differences in the same person signature may exist in The proposed Off-line Handwritten Signature details. Because of the details of an image will access recognition system as depicted in Figure 4 involves by high pass filter, DWT is used to access high pass four stages: information of person’s signature images. This information is fused to obtain pattern of each person’s Scan and removing noise stage. signatures that contains all details information of his/her Feature extraction stage. signatures [21]. Details information extracted by DWT Classification stage. technique must be extracted using suitable wavelet Test stage. function to off-line handwritten signature recognition application. According to the previous work in off-line First stage, Scan and removing noise stage, each off- handwritten signature recognition have apply line handwritten signature is scanned due to creating Daubechies 4, 12 and 20 wavelets functions as depicted signature image. Because of the scanning process, in Figure 5 [5] as a mother wavelet function, which can removing noise from signature image is an important preserve maximum details of the original image, reflect task. In this paper, the median filter [18] is used to outline of the image objectively and decrease the FRR. remove noise for two reasons. First, it preserves the structural shape of the signature without removing small strokes. Second, the absence of dealing with median filter in wavelet transform technique, which work to analysis image with low/high-pass filters corresponding to its wavelet function. db 4 db 12 db 20 The median filter is a nonlinear digital filtering technique which is often used to remove noise. Noise Figure 5: Daubechies 4, 12 and 20 wavelets functions reduction is a typical pre-processing step that improves the results. The median filter considers each pixel in the After DWT is applied on the image, wavelet image in turn and looks at its nearby neighbours to coefficients from the approximation sub-band is discard decide whether or not the pixel intensity value is and interested in wavelet coefficients from the details representative of its surroundings. The median filter sub-bands of all the decomposition levels . This entire replaces the pixel with the median of its neighbouring coefficient is very large to be used as feature extraction pixel intensity values. The median is calculated by first model from an image. These wavelet coefficients can be sorting all the pixel intensity values from the represented as statistical features such as mean, median, surrounding neighbourhood into numerical order and standard deviation, energy and entropy [22]. In this then replacing the pixel being considered with the paper, wavelet energy values for details wavelet sub- middle pixel intensity value [19]. band is the reduced vector that contain the main information that represent person signature from the Second stage, Feature extraction stage is the most huge wavelet decomposition values. important component for designing the intelligent system based on pattern recognition. The pattern space While off-line handwritten signature image is sensitive is usually of high dimensionality. The objective of the to translation, rotation and scale changes; the same feature extraction is to characterize the object by images with different scale or rotational may have reducing the dimensionality of the measurement space different wavelet coefficients. The main reason is that (i.e., the original waveform). The best classifier will the efficient implementation of 2D-DWT requires perform poorly if the features are not chosen well [20]. applying a filter bank along the rows and columns of an image [23]. Feature extraction using Wavelet energy Wavelet Transform coefficient Figure 4: Proposed off-line Handwritten signature Recognition System 4 16 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 Due to the separability of the filters, the separable 2D- The back-propagation algorithm seems to be superior in DWT is strongly oriented in the horizontal and vertical this handwritten signature verification environment directions. This makes it hardly possible to extract [25]. In a back-propagation neural network[26], the translation, rotation and scale invariant features from learning algorithm has two phases. First, a training input the wavelet coefficients. Wavelet energy can keep the pattern is presented to WNN input layer. The WNN main characteristic of these wavelet coefficients and propagates the input pattern from layer to layer until the make the same images with different translation, output pattern is generated by the output layer. If this rotation and scale having the same wavelet energy pattern is different from the desired output, an error is values[23]. Wavelet energy values can be computed calculated and then propagated backward through the after analysis signature image to it’s wavelet sub-image WNN from the output layer to the input layer. The coefficient at three level analysis (LLx, HLx, LHx, weights and both the position and dilation of the HHx). The percentages of energy of these high wavelets layer are modified as the error is propagated. frequency sub-images at the k-level wavelet The modified back-propagation training algorithm in decomposition is defined in equation (2,3,4)[24]: WNN [27]as shown in Figure 6. In this work, The input layer represents wavelet energy (����) 100 ∗ (�������� ���������������������������������������������������� ������������������������ �������� �������������������� ����)2 values feature vector to neural network. The output ������������ = (2) (���������������������������������������������������� ������������������������)2 layer represents the ability to recognize the human signature. The middle layer determined the ability to 100 ∗ (�������� ���������������������������������������������������� ������������������������ �������� �������������������� ����)2 ������������ (����) = (3) learn the person signature recognition. Because of the (���������������������������������������������������� ������������������������)2 ability of Morlet function to deal with big input domain 100 ∗ �������� ���������������������������������������������������� ������������������������ �������� �������������������� ���� 2 [28] and represents its wave form in equation, Morlet ������������ ���� = (4) function will be the suitable wavelet activation ���������������������������������������������������� ������������������������ 2 functions ψ in WNN to recognize offline handwritten signature application. Morlet function equation and it’s Third stage, Classification stage, after we get the derivation in equation (5,6)[27]: suitable wavelet energy values that represent signature image, we take this values as input to WNN and train ���� 2 ���� ���� = cos 1.75���� exp − (5) this network with a modified Back-propagation (BP) 2 training algorithm to get efficient off-line signature recognition. Using WNN for two reasons; first, Then. traditional ANN has many trade-off because of complex �������� ���� ���� 2 computations, huge iterations and learning algorithms = − ���������������� 1.75���� + 1.75 sin 1.75���� exp − (6) �������� 2 are responsible for slowing down the recognition rate using ANN; second ,recover losing important information from signature image in DWT technique because of down-sample process as depicted in Figure1. 5 17 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 Input : wavelet energy values extracted from signature image Output : Class of recognized Signature Step1: Initialize weights and offsets. Set all weights and node offsets to small random values. Initialize position and dilation parameter for each wavelet neuron in wavelet layer. To choose centre point (p)between interval [z 1,z2] (input domain),then b1=p , a1=0.5(z1-z2) Interval [z1,z2] is divided into two parts by point p. In each sub-interval, we recursively repeat the same procedure which will initialize b2, a2 and b3, a3 and so on, until all the wavelet are initialize. Step2: Present input and desired outputs Present a continuous valued input vector X1, X2…..XL and specify the desired output D1,D2,….DS. If the net is used as a classifier then all desired outputs are typically set to zero except for that corresponding to the class the input is from. That desired output is1. The input could be new on each trial or samples from a training set could be presented cyclically until stabilize. Step 3: Calculate Actual Output ���� ���� ���� ����=1 ������������ �������� − �������� ������������ = ������������ ���� 7 �������� ���� =1 Step 4: Calculate Error function ���� ���� 1 ���� = ���� (������������ − �������� )2 8 2���� ����=1 ����=1 Step 5: Propagate error to weights and position and dilation parameter ���� ���� ���� �������� 1 ����=1 ������������ �������� − �������� = ���� (������������ − �������� ) ���� 9 ���������������� ���� �������� ����=1 ���� ���� �������� 1 ��������(����) ������������ = ���� [(������������ − �������� ) ������������ ] 10 ���������������� ���� �������� �������� ����=1 ����=1 ���� ���� ����=1 ���� �������� �������� −���� ���� Where ���� = ���� ���� ���� ���� ���� ���� �������� 1 ��������(����) ����=1 ������������ �������� − �������� = ���� (������������ − �������� ) ������������ − 2 11 ������������ ���� �������� �������� ����=1 ����=1 ���� ���� �������� 1 �������� ���� 1 = ���� (������������ − �������� ) ������������ − 12 ������������ ���� �������� �������� ����=1 ����=1 Step 6: Update weights and position and dilation parameter �������� �������� �������� ������������ +1 = ������������ − ���� �������� �������� − ���� ������������ − ������������ −1 13 ���������������� where :α is learning rate ���� is momentum factor Step 7: Repeat by going to step 2 Figure 6: Back-propagation training algorithm in Wavelet Neural Network 6 18 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 Feature extraction using Wavelet energy Wavelet Transform coefficient Second Person Signature Figure 7: Testing stage of off-line Handwritten signature Recognition System Finally, Test stage, after learning WNN, we can Three WNN will be found to compare the recognition examine the ability of WNN to verify the signature of rate between tree wavelet function. Modified BP any person as shown in Figure 7.In this stage our goal training algorithm as in Figure 6 is used to train WNN. is to input signature image and recognize the person Finally, testing WNN with trained signature . Figure 9 signature. After scanning and removing noise from shows the recognition rate to (db4,db12,db20) wavelet person signature image, wavelet coefficients produce detail coefficients using WNN as mention above. As a after analysis image with DWT technique and then result from Figure 9, Db20 is recognizing to be the compute wavelet energy value from wavelet detail suitable wavelet function which have high recognition coefficients, finally, this wavelet energy values are rate in our database to offline handwritten application. taken as input to test WNN classifier to find result in output layer with only 1 value in only one neuron. Number of neurons in the output layer represents the number of person that system recognize. IV. RESLUT : This section summarizes the results of using DWT technique (wavelet energy values) as feature extraction technique and WNN as classifier to off-line handwritten signature recognition system. This paper uses nine person handwritten signatures as show in Figure 8, each person has twenty image of his handwritten Figure 9: Offline handwritten signature recognition rate using (db4,db12,db20) wavelet detail coefficients signature ,ten for train stage and ten for test stage . After determine the suitable extracting wavelet function, wavelet energy from each signature image is computed using equation 2,3,4 with db20 as wavelet function at three level analysis. Nine wavelet energy coefficients are represented each signature image. Table 1: WNN architecture and training parameters The number of layers 3 The number of neuron on the Input:9 layers Hidden:18 Output:9 Figure 8: Sample Signature images The initial weights and biases Random In feature extraction stage, wavelet detail coefficients Wavelet Activation functions Morlet function are extracted from signature image using (db4 or db 12 Learning rule Back-Propagation or db20) wavelet function. To determine the suitable MSE 0.0001 wavelet function to our database, WNN is used as a Learning rate 0.1 classifier to evaluate the suitable one. Wavelet detail coefficients (at one level analysis) of signature image Momentum factor 0.009 according to one wavelet function is taken as trained data to WNN. 7 19 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 In classification stage, WNN is used with parameters shown in Table1. These parameters are selected for WNN structure after several different experiments. In these experiments, the WNN is employed with different parameters such as the number of hidden layers, the size of the hidden layers, value of the moment constant and learning rate, and type of the activation functions. Wavelet energy values for each signature image are the input features to WNN input layer. Each neuron in WNN output layer represent a person. In the test stage , Appling the test wavelet energy Figure 11: Comparative Study between signature image pixels values of the test signature to trained WNN. Evaluating intensity value as input to ANN (ANN)and signature wavelet energy the proposed off-line handwritten signature recognition values as input to ANN (WE+ANN)and signature wavelet energy system by recognition rate to each person as shown in values as input to WNN (WE+WNN)with training data. Figure 10. Figure 12: Comparative Study between signature image pixels intensity value as input to ANN (ANN)and signature wavelet energy values as input to ANN (WE+ANN)and signature wavelet energy Figure 10: Proposed off-line handwritten signature recognition values as input to WNN (WE+WNN) testing data. system result V. CONCLUSIONS AND FUTURE WORK All system evaluation is made by two concept False Acceptance Rate(FAR) which indicates how many Handwritten signature recognition plays an important forgeries were incorrectly classified as genuine signatures ,and False Rejected Rate(FRR) which role in our daily life especially in any bank and any indicates how many genuine signatures were incorrectly ATM system. Off-Line Handwritten Signature rejected by the system. To the training signatures FAR recognition is a difficult task than On-line one because and FRR is 0.01% and to the testing signatures FAR of absence of dynamic information in off-Line signature and FRR is 0.07% . image such as angle of written style of written and so on. This paper proposed an off-Line handwritten To evaluate our proposed system a comparative study recognition system with Four stages. First stage is between three off-line handwritten signature systems is scanning signature image and removing noise using made: median filter. Second stage, extract feature from each 1-signature image pixels intensity value as input to signature image using DWT technique with the ANN(ANN) advantage of multi-scale and with respect the 2- signature wavelet energy values as input to ANN translation, rotation and scale variations of signature (WE+ANN) image. Computing wavelet energy values from DWT 3- Our proposed system signature wavelet energy details sub-bands coefficient to all person signature values as input to WNN (WE+WNN). images using the suitable wavelet function to our database. Daubechies 20 (db20) is recognize as a Figure 11 represent the recognition rate to each training person data and Figure 12 represent the suitable wavelet function with three levels analysis after a comparative study with other wavelet function. Third recognition rate to each testing person data. Figure 11 stage, taking the wavelet energy values as input to and Figure 12 concluded that our proposed system has WNN with Morlet function as activation function in the highest recognition rate. hidden layer. Finally, testing trained WNN with seen/unseen signature to evaluate our proposed system 8 20 http://sites.google.com/site/ijcsis/ ISSN 1947-5500 (IJCSIS) International Journal of Computer Science and Information Security, Vol. 8, No. 6, 2010 recognition rate. A comparative study between three [12] K R Radhika, M K Venkatesha and G N Sekhar,” Pattern Recognition Techniques in Off-line hand written signature off-line handwritten signature systems is made verification - A Survey”, PROCEEDINGS OF WORLD ( signature image pixels intensity as input to ANN , ACADEMY OF SCIENCE, ENGINEERING AND signature wavelet energy values as input to ANN and TECHNOLOGY ,vol. 36, ISSN 2070-3740 , 2008. signature wavelet energy values as input to WNN). 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Xu. “Offline signature verification Network-based Handwritten Signature Verification”, JOURNAL and identification using distance statistics”, International Journal OF COMPUTERS, VOL. 3, NO. 8, 2008. of Pattern Recognition and Artificial Intelligence. vol. 18, no.7, 2004, pp. 1339-1360. [26] Insung Jung, and Gi-Nam Wang,” Pattern Classification of Back- Propagation Algorithm Using Exclusive Connecting Network”, [8] V. Nalwa.” Automatic on-line signature verification”, Lecture World Academy of Science, Engineering and Technology,VOL. Notes In Computer Science, Proceedings of the Third Asian 36 ,2007. Conference on Computer Vision,1998, p.p 10 - 15 . [27] Ming Meng , Wei Sun,” Short-term Load Forecasting Based on [9]http://research.microsoft.com/pubs/69437/handwritingregistration_ Rough Set and Wavelet Neural Network ”, International cvpr07.pdf, 17-8-2010. Conference on Computational Intelligence and Security,2008. 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IJCSIS is an open access publishing venue for research in general computer science and information security.
Target Audience: IT academics, university IT faculties; industry IT departments; government departments; the mobile industry and computing industry.
Coverage includes: security infrastructures, network security: Internet security, content protection, cryptography, steganography and formal methods in information security; computer science, computer applications, multimedia systems, software, information systems, intelligent systems, web services, data mining, wireless communication, networking and technologies, innovation technology and management.
The average paper acceptance rate for IJCSIS issues is kept at 25-30% with an aim to provide selective research work of quality in the areas of computer science and engineering. Thanks for your contributions in September 2010 issue and we are grateful to the experienced team of reviewers for providing valuable comments.

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