1 / 19

A New Approach to Signature Verification: Digital Data Acquisition Pen

A New Approach to Signature Verification: Digital Data Acquisition Pen. Ondřej Rohlík. rohlik@kiv.zcu.cz Department of Computer Science and Engineering University of West Bohemia in Pilsen. Outline. pen – pictures, construction signals – description application areas

bryar-kidd
Download Presentation

A New Approach to Signature Verification: Digital Data Acquisition Pen

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. A New Approach to Signature Verification: Digital Data Acquisition Pen Ondřej Rohlík rohlik@kiv.zcu.cz Department of Computer Science and Engineering University of West Bohemia in Pilsen

  2. Outline • pen – pictures, construction • signals – description • application areas • signature verification • author identification • results Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  3. The Pen The pen was designed and constructed at Fachhochschule Regensburg Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  4. Writting with the Pen Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  5. Signals Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  6. Signals Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  7. Application Areas • signature verification • authentic signature or fake • person identification • which of several people • character/text recognition • replacement of keyboards and/or scanners Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  8. Signature Verification – Problem • we have to classify into two classes • classes overlaps each other • we have no training data for “fakes” Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  9. Program Developed Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  10. Useable Features Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  11. Algorithms For each class C Training algorithm For each feature f For each pair of signatures Classes[C][i] and Classes[C][j] Compute the difference between Classes[C][i] and Classes[C][j] and add it to an extra variable Sum[f] Compute mean value mean[f] and variance var[f] of each feature over all pairs using the variable Sum[f] Compute critical cluster coefficient using variances var[f] and weights w[f] over all features f For class C to be verified Recognition algorithm For each pattern Classes[c][i] For each feature f Compute the difference and remember the least one over all patterns Sum up products of least differences and weights w[f] and compare the sum with Critical cluster coefficient Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  12. Signature Verification – Results Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  13. Author Identification – Problem • samples are classified into several classes – each corresponds to one author • the written word is not a name (signature) but any other word – we use the same word for all authors Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  14. Author Identification – Problem Graphologists use many signs to characterize the personality of the author – movement (expansion in height and in width, coordination, speed, pressure, stroke, tension, directional trend, rhythm) – form (style, letter shapes, loops, connective forms, rhythm) – arrangement (patterns, rhythm, line alignment, word interspaces, zonal proportions, slant, margins – top, left and right) – signature (convergence with text, emphasis on given name or family name, placement) Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  15. Author Identification – Solution • classification by neural network – two-layer perceptron network • trained using variant of back-propagation algorithm with momentum Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  16. Author Identification – Results Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  17. Conclusion and Future Work • twofold purpose of our research: • to improve reliability of signature verification • to make text recognition devices cheaper • result achieved so far are good but more tests must be done in order to prove that our pen and methods are useful • acceleration sensor is not suitable for text recognition – will be replaced by pressure sensors Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  18. Example of signature – “Rohlík“ Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

  19. Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 2001

More Related