Uniform segmentation in online signature verification

In this paper, an online signature verification system based on the segmentation of online signature is proposed. The two set of features i.e shape features and dynamic features are extracted and a feature vector concatenating these two set of features is obtained. Euclidean distance is used as a classifier and a writer dependent threshold is used for verification purpose. In order to evaluate the effectiveness of the proposed online signature verification system, several experiments are carried out. Public database SVC 2004 is used for all the tests for online signature verification. The accuracy of proposed system with ten signatures in the training set and ten segments per signature is 91.75 % for skilled forgery. Experimentation is carried out separately for skilled forgery and random forgery. Experimental results on SVC2004 Task 2 show good performance of the proposed online signature verification system.

[1]  Loris Nanni,et al.  An On-Line Signature Verification System Based on Fusion of Local and Global Information , 2005, AVBPA.

[2]  Hussein Zedan,et al.  Rough set approach to online signature identification , 2011, Digit. Signal Process..

[3]  Julian Fiérrez,et al.  Feature Selection Based on Genetic Algorithms for On-Line Signature Verification , 2007, 2007 IEEE Workshop on Automatic Identification Advanced Technologies.

[4]  Abdul Quaiyum Ansari,et al.  Neuro-fuzzy integrated system with its different domain applications , 2012, Int. J. Intell. Syst. Technol. Appl..

[5]  Julian Fiérrez,et al.  Target dependent score normalization techniques and their application to signature verification , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Berrin A. Yanikoglu,et al.  Identity authentication using improved online signature verification method , 2005, Pattern Recognit. Lett..

[7]  L. Yang,et al.  Application of hidden Markov models for signature verification , 1995, Pattern Recognit..

[8]  Abdul Quaiyum Ansari,et al.  Analysis of two dimensional image to obtain unique statistical features for developing image recognition techniques using wavelet approach , 2011, ICWET.

[9]  Toby Berger,et al.  Reliable On-Line Human Signature Verification Systems , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  King Fahd,et al.  PERFORMANCE PREDICTION FOR PARALLEL SCIENTIFIC APPLICATIONS , 1970 .

[11]  Arun Ross,et al.  An introduction to biometric recognition , 2004, IEEE Transactions on Circuits and Systems for Video Technology.

[12]  Abdul Quaiyum Ansari,et al.  Handbook of Research on Industrial Informatics and Manufacturing Intelligence: Innovations and Solutions , 2012 .

[13]  Asghar Fallah,et al.  A new online signature verification system based on combining Mellin transform, MFCC and neural network , 2011, Digit. Signal Process..

[14]  Hamid R. Rabiee,et al.  A new online signature verification algorithm using variable length segmentation and hidden Markov models , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[15]  Swati Aggarwal,et al.  Neutrosophic classifier: An extension of fuzzy classifer , 2014, Appl. Soft Comput..

[16]  Chulhan Lee,et al.  Online signature verification using temporal shift estimated by the phase of Gabor filter , 2005 .

[17]  Abdul Quaiyum Ansari,et al.  Online signature verification using segment-level fuzzy modelling , 2014, IET Biom..

[18]  Abdul Quaiyum Ansari,et al.  Fundamentals of Industrial Informatics and Communication Technologies , 2012 .

[19]  Chirdpong Deelertpaiboon,et al.  Fusion of Local and Global Information , 2010 .

[20]  Julian Fierrez,et al.  10 On-Line Signature Verification , 2007 .

[21]  Berrin A. Yanikoglu,et al.  Online Signature Verification Using Fourier Descriptors , 2009, EURASIP J. Adv. Signal Process..

[22]  Luan Ling Lee.  Neural approaches for human signature verification , 1996, Proceedings of Third International Conference on Signal Processing (ICSP'96).

[23]  M. Hanmandlu,et al.  An Experimental Study of Different Features for Face Recognition , 2011, 2011 International Conference on Communication Systems and Network Technologies.

[24]  M. Hanmandlu,et al.  Online signature verification using GA-SVM , 2011, 2011 International Conference on Image Information Processing.

[25]  Neeraj Gupta,et al.  Echo Cancellation in Cellphone Using Neurofuzzy Filter , 2010, 2010 International Conference on Advances in Computer Engineering.