Neural network verification of dynamic signatures using pressure sensitive pen

Tham Heng Keit, P. Raveendran, Fumiaki Takeda, Yoshikazu Yoshida

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


In this paper, we present a technique to classify signatures produced by the pressure exerted on the pen tip. Before the features are exerted, a low pass filter using Sum Filter is designed to remove frequencies greater that 50 Hz. A new segmentation technique is used to divide the time series data into segments. The autoregressive (AR) coefficients are derived from each segment. From the coefficients, the power spectral density (PSD) is determined for every segment. Values from the spectral are then fed into a Multilayer Perceptron (MLP) classifier with one hidden layer for verification. A database of 1000 signatures is used for training and testing. The system is tested for genuine as well as forged signatures. The result obtained showed 2.13% error in rejecting genuine signatures and 3.40% error in accepting forged signatures.

Original languageEnglish
Title of host publicationProceedings of the 6th Joint Conference on Information Sciences, JCIS 2002
EditorsJ.H. Caulfield, S.H. Chen, H.D. Cheng, R. Duro, J.H. Caufield, S.H. Chen, H.D. Cheng, R. Duro, V. Honavar
Number of pages4
Publication statusPublished - 2002
Externally publishedYes
EventJoint Conference on Information Sciences 2002 - Research Triange Park, United States of America
Duration: 8 Mar 200213 Mar 2002
Conference number: 6th

Publication series

NameProceedings of the Joint Conference on Information Sciences


ConferenceJoint Conference on Information Sciences 2002
Abbreviated titleJCIS 2002
Country/TerritoryUnited States of America
CityResearch Triange Park


  • Burg's Autoregressive Model
  • Neural Networks
  • Signature Verification and Spectral Analysis

Cite this