Invariant directional feature extraction and matching approach for robust off-line signature verification

Off-line handwritten signature verification systems are highly required in different domains like financial transactions, forensic sciences, and legal document authentication. The proposed directional features are extracted from off-line signatures to accurately describe the angle of the pixels connection in the signature, for the reason of differentiating accurately between genuine and skilled forged signatures. Directional feature requires the adjustment of the skewness of the signature to uniform axis in the preprocessing of the signature. The accuracy of signature rotation varies according to the degree of the intra-personal variations of the signer. This work proposes a feature extraction method that generates rotation invariant directional features. The angle of each connection between pixel-pair is considered with respect to the next pixel-pair connection rather than to a global signature axis. Then matches between two different signatures by counting the longest common subsequence in these signatures. The experimental work are applied on two benchmark data sets that include skilled and genuine signatures. The accuracy of the proposed approach is between 90%-94%, outperforms the traditional SIFT/SURF based approaches.

[1]  Dinesh Dileep Gaurav,et al.  A feature extraction technique based on character geometry for character recognition , 2012, ArXiv.

[2]  Alfred Mertins,et al.  Line segment distribution of sketches for Persian signature recognition , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[3]  S. Himavathi,et al.  An Investigation on the Performance of Hybrid Features for Feed Forward Neural Network Based English Handwritten Character Recognition System , 2014 .

[4]  Lu Wang,et al.  Wide-baseline image matching using Line Signatures , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Shiwani Gupta.  Handwritten Signature Verification using Artificial Neural Network , 2015 .

[6]  Graham Leedham,et al.  Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[7]  Anastasia Rita Widiarti.  Comparing Hilditch, Rosenfeld, Zhang-Suen,and Nagendraprasad -Wang-Gupta Thinning , 2011 .

[8]  V. Muthukkumarasamy,et al.  Off-line Signature Verification Using an Enhanced Modified Direction Feature with Single and Multi-classifier Approaches , 2007, IEEE Computational Intelligence Magazine.

[9]  Jenq-Neng Hwang,et al.  Tremor detection using motion filtering and SVM , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[10]  Miguel Angel Ferrer-Ballester,et al.  On-line signature recognition through the combination of real dynamic data and synthetically generated static data , 2015, Pattern Recognit..

[11]  Rajpal Kaur.  Handwritten Signature Verification Based on Surf Features Using Hmm , 2015 .

[12]  Sumedha B. Hallale,et al.  Twelve Directional Feature Extraction for Handwritten English Character Recognition , 2013 .

[13]  Rahul Verma,et al.  Offline Signature Verification and Identification Using Angle Feature and Pixel Density Feature And Both Method Together , 2013 .

[14]  Abdul Salam Shah,et al.  An appraisal of off-line signature verification techniques , 2015 .

[15]  P. R. Shahane,et al.  Online Signature Recognition Using Matlab , 2015 .

[16]  Mohan S. Kankanhalli,et al.  Currency security and forensics: a survey , 2015, Multimedia Tools and Applications.

[17]  Michael Blumenstein,et al.  Experimental analysis of the modified direction feature for cursive character recognition , 2004, Ninth International Workshop on Frontiers in Handwriting Recognition.

[18]  M. Blumenstein,et al.  A modified direction feature for cursive character recognition , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).