DocumentCode
183452
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
fYear
2014
fDate
1-4 Sept. 2014
Firstpage
738
Lastpage
742
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on
Conference_Location
Heraklion
ISSN
2167-6445
Print_ISBN
978-1-4799-4335-7
Type
conf
DOI
10.1109/ICFHR.2014.129
Filename
6981108
Link To Document