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A New Approach to Signature Verification: Digital Data Acquisition Pen Ondřej Rohlík Department of Computer Science and Engineering University.

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Presentation on theme: "A New Approach to Signature Verification: Digital Data Acquisition Pen Ondřej Rohlík Department of Computer Science and Engineering University."— 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 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20012 Outline pen – pictures, construction signals – description application areas signature verification author identification results

3 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20013 The Pen The pen was designed and constructed at Fachhochschule Regensburg

4 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20014 Writing with the Pen

5 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20015 Signals

6 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20016 Signals

7 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20017 Application Areas signature verification authentic signature or fake person identification which of several people character/text recognition replacement of keyboards and/or scanners

8 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20018 Signature Verification – Problem - we have to classify into two classes - classes overlaps each other - we have no training data for “fakes”

9 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 20019 Program Developed

10 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200110 Useable Features

11 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200111 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

12 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200112 Signature Verification – Results

13 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200113 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

14 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200114 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)

15 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200115 Author Identification – Solution classification by neural network – two-layer perceptron network trained using variant of back-propagation algorithm with momentum

16 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200116 Author Identification – Results

17 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200117 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

18 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200118 Example of signature – “Rohlík“

19 Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM 200119


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