Angular Contour Parameterization for Signature Identification

This present work presents a parameterization system based on angles from signature edge (2D-shape) for off-line signature identification. We have used three different classifiers, the Nearest Neighbor classifier (K-NN), Neural Networks (NN) and Hidden Markov Models (HMM). Our off-line database has 800 writers with 24 samples per each writer; in total, 19200 images have been used in our experiments. We have got a success rate of 84.64%, applying as classifier Hidden Markov Model, and only used the information from this edge detection method.

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

[2]  Serestina Viriri,et al.  An off-line signature verification system , 2009, 2009 IEEE International Conference on Signal and Image Processing Applications.

[3]  Marc Parizeau,et al.  Training Hidden Markov Models with Multiple Observations-A Combinatorial Method , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[4]  Miguel Angel Ferrer-Ballester,et al.  Offline geometric parameters for automatic signature verification using fixed-point arithmetic , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Sargur N. Srihari,et al.  On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  B. Kovari,et al.  Off-Line Signature Verification Based on Feature Matching , 2007, 2007 11th International Conference on Intelligent Engineering Systems.

[7]  José B. Mariño,et al.  Speech recognition in a noisy car environment based on LP of the one-sided autocorrelation sequence and robust similarity measuring techniques , 1997, Speech Commun..

[8]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[9]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[10]  Vallipuram Muthukkumarasamy,et al.  Off-line Signature Verification Using Enhanced Modified Direction Features in Conjunction with Neural Classifiers and Support Vector Machines , 2007 .

[11]  Arun Ross,et al.  Multimodal biometrics: An overview , 2004, 2004 12th European Signal Processing Conference.

[12]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[13]  Miguel A. Ferrer,et al.  Influence of initialisation and stop criteria on HMM based recognisers , 2000 .

[14]  D.R. Hush,et al.  Progress in supervised neural networks , 1993, IEEE Signal Processing Magazine.

[15]  Gerhard Rigoll,et al.  A systematic comparison between on-line and off-line methods for signature verification with hidden Markov models , 1998, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170).

[16]  Wei Tian,et al.  Off-line Chinese Signature Verification based on Optimal Matching of Projection Profiles , 2006, 2006 6th World Congress on Intelligent Control and Automation.