Offline Signature Verification with Random and Skilled Forgery Detection Using Polar Domain Features and Multi Stage Classification-Regression Model

Offline signature verification system finds several applications in monitory transaction systems like banks. However one of the major challenges in this direction is the capability of the system to detect skilled and unskilled forgery. Many cases of bank check forgeries have been reported. Most of the offline signature verification system adopts recognition based technique where the system classifies a given signature sample as one of the samples from the database. However detection of a forgery in a given sample is challenging as the input sample looks similar to one of the samples in the database. In this paper we propose an innovative approach for offline signature verification with polar feature descriptor for signature that contains Radon Transform and Zernike Moments. Verification is performed using Multiclass Support Vector Machine. Once a signature is verified as being of a registered class, PLS Regression is applied on the sample against all samples in the database of the verified user to obtain regression score. Log Likelihood of the sample against all sample of the user is calculated using Hidden Markov Model. Authenticity of the classification is justified if the regression score and Log Likelihood distance deviation is less than 5%. Results show that the system verifies signature with an accuracy of 98% with false acceptance rate of .8%. Proposed system also detects skilled forgery with an accuracy of 71% and Random forgery with an accuracy of 76%.

[1]  S. F. Miskhat,et al.  Profound impact of artificial neural networks and Gaussian SVM kernel on distinctive feature set for offline signature verification , 2012, 2012 International Conference on Informatics, Electronics & Vision (ICIEV).

[2]  Ekta Walia,et al.  Rotation invariant complex Zernike moments features and their applications to human face and character recognition , 2011 .

[3]  D. Mohamad,et al.  Online Signature Verification Using Probablistic Modeling and Neural Network , 2012, 2012 Spring Congress on Engineering and Technology.

[4]  Siyuan Chen,et al.  A New Off-line Signature Verification Method based on Graph , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[5]  Siti Norul Huda Sheikh Abdullah,et al.  State-of-the-art in offline signature verification system , 2011, 2011 International Conference on Pattern Analysis and Intelligence Robotics.

[6]  J. Y. Lee,et al.  An online signature verification system using hidden Markov model in polar space , 2002, Proceedings Eighth International Workshop on Frontiers in Handwriting Recognition.

[7]  Mandyam D. Srinath,et al.  Combined features of cubic B-spline wavelet moments and Zernike moments for invariant character recognition , 2001, Proceedings International Conference on Information Technology: Coding and Computing.

[8]  Suneeta Agarwal,et al.  Offline signature verification using grid based feature extraction , 2011, 2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011).

[9]  K. Pavithra,et al.  Cross-validation for graph matching based Offline Signature Verification , 2008, 2008 Annual IEEE India Conference.

[10]  Ben M. Herbst,et al.  Offline Signature Verification Using the Discrete Radon Transform and a Hidden Markov Model , 2004, EURASIP J. Adv. Signal Process..

[11]  Mohamed Batouche,et al.  A novel approach for Online signature verification using fisher based probabilistic neural network , 2010, The IEEE symposium on Computers and Communications.

[12]  Abdullah I. Al-Shoshan,et al.  Handwritten Signature Verification Using Image Invariants and Dynamic Features , 2006, International Conference on Computer Graphics, Imaging and Visualisation (CGIV'06).

[13]  Marcus Liwicki,et al.  A Signature Verification Framework for Digital Pen Applications , 2012, 2012 10th IAPR International Workshop on Document Analysis Systems.

[14]  A. Julita,et al.  Online Signature Verification system , 2009, 2009 5th International Colloquium on Signal Processing & Its Applications.