Dynamic signature recognition based on velocity changes of some features

Dynamic signature analysis allows us to register individuals and their hidden human behaviour. This paper presents a stroke-based approach to dynamic analysis of signature. Individual features can be identified by finding the discrete signature points like x,y-coordinates, pressure, time and pen velocity. Between signatures, the correlation measure is determined. The dynamic features are extracted from authentic and forged signatures. Experimental results show that measurement of dynamic features (velocity changes) contains important information and offers a high level of accuracy for signature verification in comparison with the results without such measurements, which will be explained in the following parts of the paper.

[1]  Yuan Yan Tang,et al.  Off-line signature verification by the tracking of feature and stroke positions , 2003, Pattern Recognit..

[2]  Piotr Porwik,et al.  The Compact Three Stages Method of the Signature Recognition , 2007, 6th International Conference on Computer Information Systems and Industrial Management Applications (CISIM'07).

[3]  Rafal Doroz,et al.  Signature characteristic points determination by means of the IPAN99 algorithm , 2007 .

[4]  Jan P. Allebach,et al.  Application of Principal Components Analysis and Gaussian Mixture Models to Printer Identification , 2004, NIP & Digital Fabrication Conference.

[5]  Nidal S. Kamel,et al.  Glove-Based Approach to Online Signature Verification , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Steven K. Feiner,et al.  Introduction to Computer Graphics , 1993 .

[7]  Héctor M. Pérez Meana,et al.  Dynamics features Extraction for on-Line Signature verification , 2004, CONIELECOMP.

[8]  Y. Y. TANG,et al.  Offline Signature Verification by the Analysis of Cursive Strokes , 2001, Int. J. Pattern Recognit. Artif. Intell..

[9]  Kosin Chamnongthai,et al.  Off-line signature recognition using parameterized Hough transform , 1999, ISSPA '99. Proceedings of the Fifth International Symposium on Signal Processing and its Applications (IEEE Cat. No.99EX359).

[10]  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).

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

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

[13]  Haris Baltzakis,et al.  A new signature verification technique based on a two-stage neural network classifier , 2001 .

[14]  Venu Govindaraju,et al.  ER2: an intuitive similarity measure for on-line signature verification , 2004, IWFHR.

[15]  Keinosuke Fukunaga,et al.  Introduction to statistical pattern recognition (2nd ed.) , 1990 .

[16]  Flávio Bortolozzi,et al.  Off-line signature verification using HMM for random, simple and skilled forgeries , 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition.

[17]  Nidal Kamel,et al.  Dynamic Signature Verification Using Sensor Based Data Glove , 2006 .

[18]  J. Jackson,et al.  Orthogonal least squares and the interchangeability of alternative , 1988 .

[19]  Khalid Saeed.  Object classification and recognition using Toeplitz matrices , 2003 .