Authentication of Offline Signatures Based on Central Tendency of Features and Dynamic Time Warping Values Preserved for Genuine Cases

This work proposes to authenticate offline signatures using a Case-Based Reasoner (CBR). The case base serves as a repository of sets of genuine signatures for which a central point on the n-dimensional global feature space is preserved along with the Inter-Quartile Range (IQR). These signatures are paired off to perform Dynamic Time Warping (DTW) comparison on their respective contours. Metrics generated from the global features and DTW values for the preserved signatures are utilized to predict authenticity of test signatures. Philosophically, CBR is a good classifier since it does not need any training by forgery models. The overall accuracy of the CBR classifier is maintained at a reasonably high value as a larger False Rejection Rate (FRR) is compensated by a tight False Acceptance Rate (FAR) value when compared with a MLP classifier. Both the classifiers have been tested on a standard offline signature database as well as one collected and prepared during the current research.

[1]  Brian C. Lovell,et al.  An Automatic Off-Line Signature Verification and Forgery Detection System , 2008 .

[2]  S. Chiba,et al.  Dynamic programming algorithm optimization for spoken word recognition , 1978 .

[3]  Jarrod Trevathan,et al.  Neural Network-based Hwritten Signature Verification , 2008, J. Comput..

[4]  Hong-Yuan Mark Liao,et al.  Wavelet-Based Off-Line Handwritten Signature Verification , 1999, Comput. Vis. Image Underst..

[5]  Hong Yan,et al.  Off-line signature verification based on geometric feature extraction and neural network classification , 1997, Pattern Recognit..

[6]  Isao Yoshimura,et al.  An Application of the Sequential Dynamic Programming Matching Method to Off-Line Signature Verification , 1997, BSDIA.

[7]  Joseph W. Goodman,et al.  Neural networks and handwritten signature verification , 1991 .

[8]  S. Pal,et al.  Foundations of Soft Case-Based Reasoning: Pal/Soft Case-Based Reasoning , 2004 .

[9]  H. Baltzakisa,et al.  A new signature verification technique based on a two-stage neural network classifier , 2000 .

[10]  Dirk Herrmann,et al.  Foundations Of Soft Case Based Reasoning , 2016 .

[11]  Sanjay N. Gunjal,et al.  Robust Offline Signature Verification Based on Polygon Matching Technique , 2011 .

[12]  Siyuan Chen,et al.  Machine Learning for Signature Verification , 2006, ICVGIP.

[13]  A. N. Rajagopalan,et al.  Off-line signature verification using DTW , 2007, Pattern Recognit. Lett..

[14]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[15]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[16]  Simon C. K. Shiu,et al.  Foundations of Soft Case-Based Reasoning: Pal/Soft Case-Based Reasoning , 2004 .