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1st Dheeraj D Research Scholar,VTU RRCAsst Prof, Dept. of ISE, Global Academy of Technology,Bengaluru, [email protected] Signatures is one of the popular biometric that depicts or reveals the attribute of humans which are extensively being used to authorize and authenticate day to day needs. Signatures play a very crucial role since they are the most critical piece of information that are used by humans for financial and commercial applications and legal transactions. Even today we have been practicing the traditional culture of signing documents like cheques and legal papers. Therefore, it is becomes necessity to recognize these signatures accurately with minimal effort in an accurate manner which becomes tedious by using the traditional manual verification technique. Thus there is a requirement to develop an automated signature recognition system. Verification systems can be designed either as an offline or online system based on the application. This work here focuses on offline authentication system that accepts as image a scanned signature. Hence comes the need to perform pre-processing which on an offline system is too complex to implement since the stable dynamic characteristics would be missed out once a scanned image is used for analysis. Also since signatures are truly unconventional and mainly styled representations need arises to design a reliable signature authentication system that can correctly classify signatures to their appropriate classes namely genuine and forged. Finally the performance analysis has been carried out on the local and global datasets and the accuracy has been tabulated using two different classifiers. It is also observed in the work that intra personal variations are recorded and accuracy of 66 is obtained for linear classifier and 74 for logistic classifier. Keywords Offline signature, Voronoi, Linear Regression, SVM, Statistical Classifier. Introduction Signature has been one of the prime unique feature to reveal a persons identity. Today most of the applications in business and finance are being authorized via signatures. Signature, is derived from Signare ,a latin word which means Sign5.Signatures are stylish marker representation that reveals a persons identity uniquely written on documents. The signature can be defined as follows the name of a person written with his or her own hand or the act of signing ones name 2, according to the American Heritage Dictionary. A signer writes on documents as a mark of his identity. The first legalization of the handwritten signature has been declared by the British government in the 19th century8. A signature is made up of special characters that are written habitually often in unreadable state. Handwritten signatures are now official means to depict the identity of person legally on wills, bank cheques, credit cards etc. The signatures are being used extensively to authenticate the personnel identify widely because of the non-invasive process of collecting the human handwritten signatures. Also it is accepted to be the most familiar authenticated style people use for their daily transactions on need basis. MOTIVATION There is an increasing need for developing a system which is fool proof and can detect maximum forged signatures. This potential security reaps benefits for automatic cheque clearing in banking industry. Signature authentication in this project is executed by determining the difference between an reference signature and a counterfeit one. In our work the prime focus is on offline systems keeping in mind the situation of a least bank that can afford for the system i.e. retaining hard copies of signatures on specimen forms. This approach is more apt in cases where authentication is to be applied for huge documents. SCOPE The project deals with static images of handwritten signatures. The genuine signature samples taken from known writes and the forgery set were generated imitating the genuine set. OBJECTIVE Handwritten signatures of every individual may be obtained by taking specimen copies of their signature by hand and then scanned to form an image. Alternatively, a camera can be used to capture these signatures. As a result during the capture using these devices may result in noisy images. The noise may be generated due to photometric and geometric constraints. The noise can also be due to hardware constraints as well .Therefore these images that contain noise cannot be used for analysis and thus need arises to pre-process these images before extracting features. The features are extracted locally and globally on the dataset of the individual and a feature vector is designed which are then passed as input to the classifiers to introspect their validity. The classifier decides whether the signature is genuine or forgery. problem statement Signatures are the nave sought authentication technique used for authorizing legal documents and banking transactions. Thus need arises to design an efficient automated solution for signature verification since fraud detection has reached its peek in real time wherever signatures are the piece of authorizing individual. Therefore, the objective of this work is to develop a robust automated system that can categorize the signatures based on some trivial features into a genuine class or a forged class. This system may showcase acceptable trade-off between the acceptable and non-acceptable signatures into their desired category. To measure this indifference which may hinder the performance of the system, we propose to use standard quality measures such as FAR and FRR to measure the acceptance and rejection parameter values uniquely for every signature database in the system respectively. Literature Survey E.N. Zois et al. 1 developed a new approach for off-line signature recognition and verification where a subset of the line, concave and convex family of curvature features is used to represent the signatures. Segmentation of the signature trace is enabled using a window which is centered upon the centre of mass of the thinned image. Partitioning of the image leads to a multidimensional feature vector which provides useful spatial details of the acquired handwritten image. A hard margin support vector machine is used for classification. Prashanth et al. 2 proposed an off-line signature verification system based on scores correlation. Feature points based on vertical splitting, and feature points based on horizontal splitting are extracted. The signature image is split with a vertical line passing through the geometric centre of the image to get its left and right parts. This geometric centre is obtained by locating a point where the number of black pixels is half of the total number of black pixels in the signature. The signature image is split with a horizontal line passing through the geometric centre to get the top and bottom parts of the image. Parodi et al. 3 gave new technique for rotation invariant feature extraction based on a circular grid. Graphometric features for the circular grid are defined by adapting similar features available for rectangular grids, and the property of rotation invariance of the Discrete Fourier Transform (DFT) is used in order to achieve robustness against rotation. Support Vector Machine (SVM) based classifier scheme is used for classification tasks. This verification system has a performance comparable to similar state-of-art signature verification systems with the additional advantage of being robust against rotation of the signatures. K. Tselios et al. 4 proposed an algorithm that models the handwritten elements of a signature trace by probabilistically counting the distribution of fixed two- and three-step pixel paths, conditioned that they are confined within predetermined Chebyshev distances of two and three, respectively. This representation correlates the pixel transitions along the signature trace, with the writing style of an individual. Similarity scores and hard margin support vector machines (SVMs) are combined or evaluated as separate entities. Results, derived from random or simple imitations as well as skilled forgery indicate that the proposed method achieves noticeably low equal rates and it is expected to provide a powerful discriminative representation of signature. Karounia et al. 5 proposed an offline signature recognition system using neural networks approach. The authors presented a method for offline verification of signatures using a set of simple shape based geometric features. The features that are used are area, center of gravity, eccentricity, kurtosis and skewness. Before extracting the features, pre-processing a scanned image is necessary to isolate the signature part and to remove any spurious noise present. The system is initially trained using a database of signatures obtained from those individuals whose signatures have to be authenticated by the system. proposed methodology The proposed system concentrates on designing an algorithm which is used to classify the signature into accepted or rejected category based on the input signature sample used for training. The proposed work is carried out in phases and the results are recorded accordingly. The first part talks about how the signature database was built, followed by second part which describes how the obtained raw signature image is shaped into an image ready for analysis i.e. pre-processing. Then finally feature extraction techniques are used to extract relevant features that are used for training the recognition system.Lastly, by applying 80-20 cross validation technique the entire signature database is divided into reference and probe images randomly of which a randomly a test image chosen which will also have to go through the entire process and features are extracted. The feature values of the reference image and the probe image are compared resulting in a decision of fixing a threshold value on which is the entire decision of categorizing the signature sample as class 0 or class 1 is evident in the experimentation by rightly classifying them as accepted or rejected signature sample. Fig. 1 depicts the block diagram of the proposed signature classification system. Figure 1 Block diagram for Offline Signature Classification System DATAFLOW DIAGRAM Figure 2 Context flow diagram for Level 0 Figure 3 Dataflow diagram showing 1st level detail RESULTS FEATURE EXTRACTION LENGTH PARAMETERS DESCRIPTION Width and height enables us determine the area of the signature, which further helps in determining the other features based on the length. The ratio of width to height is known as aspect ratio and is calculated for every the signature image. OUTPUT PIXEL RATE DESCRIPTION Each image is made up of collection of pixels. The value of each pixel may range between 0 and 255. OUTPUT NORMALISED AREA OF SIGNATURE DESCRIPTION Normalization is a method used to measure the change in pixel intensity values and record the same to plot in range of 0 to 1.In our proposed work, Min-Max normalization technique is applied to specifically fit the data within the boundary decided. OUTPUT MEAN, VARIANCE AND STANDARD DEVIATION DESCRIPTION Mean- The average of sum of all the values in the matrix. Variance- The average of the squared differences from the Mean. SD- It measures the amount of variation or dispersion on a set of mean data values. OUTPUT HISTOGRAM DESCRIPTION A histogram is an accurate representation of the distribution of numerical data. A histogram is a graph. A Histogram has two axis the x axis and the y axis. The x axis contains event and the y axis represents frequency. HORIZONTAL AND VERTICAL HISTOGRAM Represents the column-wise and row-wise histogram respectively. Histograms represent column-wise and row-wise the sum of differences in gray level values which are measured with adjacent pixels by which image is made up. VORONOI DIAGRAM Voronoi diagram is used to partition a plane in accordance with distance to points plotted on a specific subset plane into regions. In our work carried out, the seed points are decided in advance and also information about to which region should each seed correspond to looking into those points which is close to that defined seed than to any other in the defined region cell is termed as Voronoi cells. CLASSIFICATION Classification involves identifying a unknown input sample into a category of class it belongs to based on training data set in which we have already recorded the values and the class to which the signature samples belong to. Classification can be achieved through supervised technique or an unsupervised technique depending on the mode of operation. In our problem, classifier accepts a input signature and then performs analysis to announce as to which class number the input signature sample belongs to i.e class 0 or class 1. Thus the major task of signature identification in our project is to recognize the actual class to which unknown input signature should belong to. To perform this task, it should be noted that input sample signatures class must match exactly to that of the closest sample. After feature extraction, the features extracted from both genuine and forgery signatures are tabulated that as to be classified using some classifier. In statistical classifier, analyzing each feature values in feature tabulated table the suitable threshold for each feature per person is chosen. Based on the obtained values in comparison with fixed tthreshold values, system detects the signature and classifies them either under genuine label or forged label. Linear Classifier Linear classifier identifies as to which class the characteristics of object belongs to and based on these values a new vector called feature vector is formed and is supplied to the machine for making decision.Accuracy levels are better as comparable to non-linear classifiers. Most of the algorithms used for classification operate in based on linear function.Then a score for every k category is calculated on each instance. Now dot product is combined with with a vector of weights and the feature vector formed for every instance. Then through these training samples the machine learns the weight vector and the category with the highest score is the predicted linear function.The linear classifier operates by splitting the high dimensional space as input with hyperplane. The points that lie on one side of the hyperplane splitting line are classified as YES class and others into no class. A linear classifier is fastest and widely accepted classifier for sparse data. Logistic Regression The logistic model is a binary statistical model which accepts as input a continuous independent variable and delivers the output as a dependent variable. It is observed that a unit change in the input possibly multiplies the odds of the two outputs by a constant factor. This binary logistic classifier with the help of sigmoid function can estimate the binary response probability through one or more features that are mostly predictor variables. The experiments are conducted on dataset containing 800(local) signatures and 1000(global) signatures. The signatures contain both genuine and forged signatures. Output layout Statistical Classifier The pseudo code is as follows Step 1 Features are loaded from the excel sheet. Step 2 Threshold value for each feature is fixed. Step 3 Given the test input, extract the feature and record them in the excel sheet. Again, read the features. Compare each feature with each threshold value. Step 4 If both matches then the given input signature is supposed to be genuine else forgery. Linear Classifier The pseudo code is as follows Step 1 Read the features the excel sheet. Step 2 All features are given to an array list, i.e x Step 3 Define a label such that 1 is for genuine and 0 is for forgery, i.e y Step 4 Using the function clf.fit(x,y), fit the points on the hyperplane where x is array and y is label. Step 5 Linear classifier plots the graph according to the points. Figure .4 linear classifier for genuine signature Figure . 5 linear classifier for forgery signature Logistic Regression The pseudo code is as follows Step 1 Read the features from the excel sheet. Step 2 Each feature is given to an array namely x1, x2, x3 Step 3 Define a label such that 1 is for genuine and 0 is for forgery. Step 4 Append each array to the list. Step 5 Initialize by fitting the number of features i.e theta Step 6 Compute cost_function, gradient and sigmoid function. Display all the values. PERFORMANCE ANALYSIS OF INDIVIDUAL PERSON False Rejection Rate False RR Total GRP / Total GSP, where GRP is the total rejected patterns that are genuine, GSP is the total genuine reference patterns that are submitted. False Accept Rate False AR ASF / SSF, where ASF is total forgeries that are accepted, and SSF is the total forgeries that are submitted. PERFORMANCE EVALUATION Conclusion The proposed system involves the pre-processing of an input image which necessarily is a signature of an individual person. The images are pre-processed by cropping the image, dilating it and further filtering the same. The features including the width, height, area, number of black and white pixels, mean, variance and standard deviation are extracted. All the features are tabulated for all the signatures, including the signature variants of the same person, then dominant features are selected from the tabulated results and apply linear classifier and logistic regression for the classification of signature as genuine or forged category. From the results obtained from the trained signatures, the FAR and FRR the FAR and FRR are calculated and found to be 6.24 and 5.84 respectively for linear classifier and 7.38 and 4.65 respectively for logistic regression. The accuracy of the proposed signature comes approximately 66 for linear classifier and 74 for logistic regression. The results encourage us towards the future refinement. The accuracy attained so are not satisfactory and more focus is required on offline-signature verification system. Future work concentrates on hybrid algorithms to enhance the accuracy. The project is done using linear classifier considering only two features. It can be enhanced so that it can consider all the features extracted using some powerful classifier. References E. N. Zois, A.Nassiopoulos, K. Tselios, E. Siores, G. Economou OffLine Signature Verification Using Two Step Transitional Features, MVA2011 IAPR Conference on Machine Vision Applications, June 1315, 2011, Nara, JAPAN pp 295-298. Karounia A., Dayab B., Bahlakb S., 2011, Offline signature recognition using neural networks approach, Elsevier Procedia Computer Science, 3, pp. 155161. Radhika K.R., Venkatesha M.K., Sekhar G.N., 2010, Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine, Journal of Computer Science, 6(3), 2010, pp. 305311. Ebrahimpour R., A. Amiri, M. Nazari, A. Hajiany, 2010, Robust Model for Signature Recognition Based on Biological Inspired Features, International Journal of Computer and Electrical Engineering, 2(4), pp. 622626. J. B. Fasquel and M. Bruynooghe. 2004. A hybrid optoelectronic method for fast off-line handwritten signature verification. International Journal on Document Analysis and Recognition (2004). Ahmad S M S, Shakil A, Faudzi M A, Anwar R M and Balbed M A M. 2009. A Hybrid Statistical Modeling, Normalization and Inferencing Techniques of an Off-line Signature Verification System. 2009 World Congress on Computer Science and Information Engineering. Bhuyan M, Sarma K K, Das H. 2010. Signature Recognition and Verification using Hybrid Features and Clustered Artificial Neural Network (ANN)s. World Academy of Science, Engineering and Technology 68 (pp 451-456). M. Tomar, and P. Singh, A directional feature with energy based offline signature verification network, International Journal on Soft Computing, vol.2, pp. 4857, February 2011, doi 10.5121/ijsc.2011.2105. R. Anjali, and M.R. Mathew, An efficient approach to offline signature verification based on neural network, IJREAT International Journal of Research in Engineering Advanced Technology, vol.1, pp. 15, June-July 2013. V.M. Deshmukh, and S.A. Murab, Signature recognition verification using ANN, International Journal of Innovative Technology and Exploring Engineering, vol.1, pp. 68, November 2012. A. Pansare, and S.Bhatia, Handwritten signature verification using neural network, International Journal of Applied Information Systems, vol.1, pp. 4449, January 2012 0_R3Nm9Ra AKSPuf_Kd3EFfw WGwS @Id3aoltn5_s5M-bLB620066 xjn_42hhUvxdzkmzEdqJ)3i/-TQ(VUzOOBvJ jiyM -8qV4Ss3kO 7PpCn71 PxIq [email protected])[email protected] /Q IHTJMLlh FbP7lF(ct
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