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Signature recognition

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  • 1. Presented By Vijayalakshmi.S.L Under the Guidance of Mrs.Smita Gour Department of Computer Science & Engineering Basaveshwar Engineering College Bagalkot
  • 2. Contents  Introduction  Literature Survey  Problem Definition  Proposed Methodology  Experimentations  Conclusion and future work References
  • 3. Introduction  Nowadays, person identification (recognition) and verification is very important in security and resource access control.  Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes.  For centuries, handwritten signatures have been an integral part of validating business transaction contracts and agreements.  Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used.
  • 4. Signature Recognition  Signature Recognition is the procedure of determining to whom a particular signature belongs to.  Depending on acquiring of signature images, there are two types of signature recognition systems:  Online Signature Recognition  Offline Signature Recognition
  • 5. Literature Survey 1. Offline Handwritten Signature Recognition(Gulzar A. Khuwaja and Mohammad S. Laghari)  Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capability to reliably distinguish between an authorized person and an imposter.  This paper presents a neural network based recognition of offline handwritten signatures system that is trained with low- resolution scanned signature images.
  • 6. 2. Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks(Shashi Kumar D R and K B Raja)  In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features using Neural Network(SVFGNN) is presented.  The global and grid features are fused to generate set of features for the verification of signature.
  • 7. 3. DWT based Off-line Signature Verification using Angular Features (Prashanth C R )  This papers presents DWT based Off-line Signature Verification using Angular Features (DOSVAF).  The signature is resized and Discrete Wavelet Transform (DWT) is applied on the blocks to extract the features.
  • 8. 4. Off-Line Signature Recognition Systems(V A Bharadi)  Handwritten signature is one of the most widely used biometric traits for authentication of person as well as document.  In this paper we discuss issues regarding off-line signature recognitions.  The performance metrics of typical systems are compared along with their feature extraction mechanisms.
  • 9. 5. Offline Signature Recognition and Verification Based on Artificial Neural Network(Mohammed A. Abdala)  In this paper, a problem for Offline Signature Recognition and Verification is presented.  A system is designed based on two neural networks classifier and two powerful features (global and grid features).  The designed system consist of three stages which is pre- processing, feature extraction and neural network stage.
  • 10. 6. Signature Recognition & Verification System Using Back Propagation Neural Network (Nilesh Y. Choudhary, Dr. Umesh. Bhadade)  In this paper, off-line signature recognition & verification using back propagation neural network is proposed which is based on steps of image processing, invariant central moment & some global properties and back propagation neural networks.
  • 11. Problem Definition Signature Recognition is the procedure of determining to whom a particular signature belongs to. In this work, the global and grid features are combined and used to differentiate among the signature images. These combined features are given to Back Propagation Neural Network(BPNN) to train it, so that particular signature image is recognized.
  • 12. Proposed Model Block Diagram of Signature Recognition
  • 13. Image Acquisition :  Collection of signatures from 50 persons on blank paper.  The collected signatures are scanned to get images in JPG format to create database.
  • 14. Pre-Processing : Image pre-processing is a technique to enhance raw images received from cameras/sensors placed on satellites, space probes and aircrafts or pictures taken in normal day-to-day life for various applications. The techniques for preprocessing used are  RGB to Gray Scale Conversion  Binarization  Thinning  Bounding Box
  • 15.  RGB to Gray-Scale Convertion  Binarization RGB Image Gray-Scale Image Gray-Scale Image Binarized Image
  • 16.  Thinning  Bounding Box Binarized Image Thinned Image Thinned Image Bounded Image
  • 17. Feature Extraction Features are the characters to be extracted from the processed image. It has used two feature techniques  Global Features  Grid Features
  • 18. Global Features  Height :  Width :  Number of Black Pixels :  Centroid of the signature : Width Height
  • 19. Grid Features  The cropped image is divided into 9 rectangular segments i.e. (3 Χ 3) blocks. 3*3 Blocks of Grid Image
  • 20. DWT(Discrete Wavelet Transform)  DWT applied on 1st block. Each block contributes horizontal, vertical and diagonal components. 1st Block Horizontal Vertical Diagonal
  • 21.  After applying DWT to all 9 blocks, each block is divided into horizontal, vertical and diagonal components. From each components two features mainly horizontal and vertical projection positions are extracted. Total 54 (9 x 3 x 2) features are extracted. Grid features extracted from each block are  Horizontal Projection Position:  Vertical Projection Position:
  • 22. Total 54 features extracted by 9 blocks
  • 23. Classification  What is Neural Network..?  Why Neural Network..?  What is Back Propagation Neural Network(BPNN)…?
  • 24. BPNN Architecture Architecture of Back Propagation Neural Network
  • 25. Training of BPNN This involves developing a suitable neural network model (BPNN). Then the extracted features are presented to BPNN, which recognizes the different types of signature images. The training takes place such that the neural network learns that each entry in the input file has a corresponding entry in the output file.
  • 26. Run Snapshot of BPNN
  • 27. Algorithm for Training phase Description: Retrieval of a signature image from a database Input: Training sample images. Output: Construction of Back Propagation Neural Network. Begin Read the training samples images Step1: Pre-processing  Convert the image into gray scale image.  Convert the gray scale image into binary image.  Apply thinning process.  Apply bounding box.
  • 28. Step 2: Features Extracted. Step 3: Back propagation neural network training. end // end of proposed algorithm
  • 29. Testing using Trained BPNN In testing, input image from testing set is selected and its features are extracted and given them to the trained model, the trained BPNN model classifies given sample and produces output as type of signature and corresponding pattern Classification accuracy= Number of recognized signatures Total number of testing signatures
  • 30. Output Pattern for Recognition
  • 31. Experimental Results Experiment 1 The features extracted are listed as:  Height of the signature  Width of the signature  Centroid of X-axis and Y-axis  Number of black pixels of the signature  The image is divided into 9 blocks and DWT is applied to each block. Energy values of each block were extracted as a feature.
  • 32. 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 83.46 81 78.28 76.7 74 Performance Rate Performance Rate No of Persons Performance Rate of 1st Experiment
  • 33. Experiment 2 The features are extracted as listed below:  Height of the signature  Width of the signature  Centroid of X-axis and Y-axis  Number of black pixels of the signature  The image is divided into 9 blocks and DWT is applied to each block. From each block two features, horizontal and vertical projection positions of horizontal, vertical and diagonal components are extracted
  • 34. 10 20 30 40 50 60 70 80 90 100 10 20 30 40 50 93.33 92.91 91.38 90 89.47 Performance Rate Performance Rate No of Persons Performance Rate of 2nd Experiment
  • 35. No. of Persons Experiment 1 Experiment 2 10 83.46 % 93.33 % 20 81 % 92.91 % 30 78.28 % 91.38 % 40 76.7 % 90 % 50 74 % 89.47 % Performance Rate The performance rate of the two experiments
  • 36. Conclusion The objective of signature recognition is to recognize the signer for the purpose of recognition. It has been observed that the global and grid features extracted using discrete wavelet transform are found to be efficient for offline signature recognition. The combination of discrete wavelet transform and back propagation neural network has given expected results. It achieved the accuracy rate ranging from 93%-89% for enrollment of 10 to 50 persons.
  • 37. Future Work The signature recognition can also be changed by changing the features that can be extracted from a signature. So, the future work of the recognition of signature can be done with the same Neural Network methods but using different signature features and compares the results with results of the present project.
  • 38. References  Gulzar A. Khuwaja and Mohammad S. Laghari, World Academy of Science, Engineering and Technology , “Offline Handwritten Signature Recognition”, 2011  Shashi Kumar D R, K B Raja, R. K Chhotaray, Sabyasachi Pattanaik, “Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks”, 2010  Prashanth C R , K B Raja, Venugopal K R, L M Patnaik, “DWT based Off-line Signature Verification using Angular Features”, 2012  V A Bharadi, H B Kekre, “Off-Line Signature Recognition Systems”, 2010  Mohammed A. Abdala & Noor Ayad Yousif, “Offline Signature Recognition and Verification Based on Artificial Neural Network”, 2008  H. Baltzakis, N. Papamarkos, “A New Signature Verification Technique Based On A Two-Stage Neural Network Classifier”, 2001  Khamael Abbas Al-Dulaimi, “Handwritten Signature Verification Technique based on Extract Features”, 2011
  • 39.  Hemanta Saikia, Kanak Chandra Sarma, “Approaches and Issues in Offline Signature Verification System”, 2012  Vu Nguyen, Michael Blumenstein, Graham Leedham, “Global Features for the Off-Line Signature Verification Problem”, 2009  Meenakshi S Arya, Vandana S Inamdar, “A Preliminary Study on Various Off-line Hand Written Signature Verification Approaches”, 2010  Javed Ahmed Mahar, Prof. Dr. Mumtaz Hussain Mahar, Muhammad Khalid Khan, “Comparative Study of Feature Extraction Methods with K-NN for Off- Line Signature Verification”, 2006  Nilesh Y. Choudhary, Mrs. Rupal Patil, Dr. Umesh. Bhadade, Prof. Bhupendra M Chaudhari,“Signature Recognition & Verification System Using Back Propagation Neural Network”, 2013  Manoj Kumar, “Signature Verification Using Neural Network”, 2012  Paigwar Shikha and Shukla Shailja,“Neural Network Based Offline Signature Recognition and Verification System”, 2013  Srikanta Pal, Michael Blumenstein, Umapada Pal, “Off-Line Signature Verification Systems: A Survey”, 2011
  • 40. Thank You