Online signature verification using Fourier descriptors
EURASIP Journal on Advances in Signal Processing - Special issue on recent advances in biometric systems: a signal processing perspective
Trainable sketch recognizer for graphical user interface design
INTERACT'07 Proceedings of the 11th IFIP TC 13 international conference on Human-computer interaction
Determining the similarity of signatures on the basis of characteristic points analysis
International Journal of Biometrics
Ergodic HMM-UBM system for on-line signature verification
BioID_MultiComm'09 Proceedings of the 2009 joint COST 2101 and 2102 international conference on Biometric ID management and multimodal communication
Enhancing iris matching using levenshtein distance with alignment constraints
ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Signature recognition using artificial neural network
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
A sketching tool for designing anyuser, anyplatform, anywhere user interfaces
INTERACT'05 Proceedings of the 2005 IFIP TC13 international conference on Human-Computer Interaction
A study on enhanced dynamic signature verification for the embedded system
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
A visualization based approach for digital signature authentication
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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In this paper a new method for on-line signature authentication will be presented, which is based on a event-string modelling of features derived from pen-position and pressure signals of digitizer tablets. A distance measure well known from textual pattern recognition, the Levenshtein Distance, is used for comparison of signatures and classification is carried out applying a nearest neighbor classifier. Results from a test set of 1376 signatures from 41 persons are presented, which have been conducted for four different feature sets. The results are rather encouraging, with correct identification rates of 96% at zero false classifications.