Title :
Online Signature Verification Based on Kolmogorov-Smirnov Distribution Distance
Author :
Griechisch, Erika ; Malik, Muhammad Imran ; Liwicki, Marcus
Author_Institution :
MTA-SZTE Res. Group on Artificial Intell., Univ. of Szeged, Szeged, Hungary
Abstract :
Online signature verification methods examine the dynamics of the handwriting process to decide whether a signature is probably genuine or forged. Most of the previously proposed methods for online signature verification apply Neural Networks, Dynamic Time Warping, or Hidden Markov Model for classification and they consider several aspects, like planar coordinates, pressure, velocity, and acceleration with respect to time. Here we apply a non-parametric statistical test for a comparison of features and the verification of signatures.
Keywords :
digital signatures; feature extraction; handwriting recognition; hidden Markov models; neural nets; statistical distributions; time warp simulation; Kolmogorov-Smirnov distribution distance; dynamic time warping; feature extraction; handwriting process dynamics; hidden Markov model; neural networks; online signature verification; Distribution functions; Error analysis; Handwriting recognition; Hidden Markov models; Neural networks; Time factors; distance measures; handwritten signatures; online signature verification; statistics;
Conference_Titel :
Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on
Conference_Location :
Heraklion
Print_ISBN :
978-1-4799-4335-7
DOI :
10.1109/ICFHR.2014.129