Online handwritten signature verification using neural network classifier based on principal component analysis

Vahab Iranmanesh, Sharifah Mumtazah Syed Ahmad, Wan Azizun Wan Adnan, Salman Yussof, Olasimbo Ayodeji Arigbabu, Fahad Layth Malallah

Research output: Contribution to journalArticle

12 Citations (Scopus)

Abstract

One of the main difficulties in designing online signature verification (OSV) system is to find the most distinctive features with high discriminating capabilities for the verification, particularly, with regard to the high variability which is inherent in genuine handwritten signatures, coupled with the possibility of skilled forgeries having close resemblance to the original counterparts. In this paper, we proposed a systematic approach to online signature verification through the use of multilayer perceptron (MLP) on a subset of principal component analysis (PCA) features. The proposed approach illustrates a feature selection technique on the usually discarded information from PCA computation, which can be significant in attaining reduced error rates. The experiment is performed using 4000 signature samples from SIGMA database, which yielded a false acceptance rate (FAR) of 7.4% and a false rejection rate (FRR) of 6.4%.

Original languageEnglish
Article number381469
JournalScientific World Journal
Volume2014
DOIs
Publication statusPublished - 01 Jan 2014

Fingerprint

Principal Component Analysis
Principal component analysis
principal component analysis
Classifiers
Neural networks
Neural Networks (Computer)
Databases
Multilayer neural networks
Set theory
Feature extraction
rate
experiment
Experiments

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Environmental Science(all)

Cite this

Iranmanesh, Vahab ; Ahmad, Sharifah Mumtazah Syed ; Adnan, Wan Azizun Wan ; Yussof, Salman ; Arigbabu, Olasimbo Ayodeji ; Malallah, Fahad Layth. / Online handwritten signature verification using neural network classifier based on principal component analysis. In: Scientific World Journal. 2014 ; Vol. 2014.
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Online handwritten signature verification using neural network classifier based on principal component analysis. / Iranmanesh, Vahab; Ahmad, Sharifah Mumtazah Syed; Adnan, Wan Azizun Wan; Yussof, Salman; Arigbabu, Olasimbo Ayodeji; Malallah, Fahad Layth.

In: Scientific World Journal, Vol. 2014, 381469, 01.01.2014.

Research output: Contribution to journalArticle

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