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

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

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

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

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

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

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

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

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

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

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

Ondřej Rohlík, University of West Bohemia in Pilsen - SoftCOM