On-line Signature Verification Based on GA-SVM

Authors

  • Dong Huang
  • Jian Gao

DOI:

https://doi.org/10.3991/ijoe.v11i6.5122

Keywords:

Online Signature Verification, SVM, Data Description, Genetic Algorithm, Feature Selection

Abstract


With the development of pen-based mobile device, on-line signature verification is gradually becoming a kind of important biometrics verification. This thesis proposes a method of verification of on-line handwritten signatures using both Support Vector Data Description (SVM) and Genetic Algorithm (GA). A 27-parameter feature set including shape and dynamic features is extracted from the on-line signatures data. The genuine signatures of each subject are treated as target data to train the SVM classifier. As a kernel based one-class classifier, SVM can accurately describe the feature distribution of the genuine signatures and detect the forgeries. To improving the performance of the authentication method, genetic algorithm (GA) is used to optimise classifier parameters and feature subset selection. Signature data form the SVC2013 database is used to carry out verification experiments. The proposed method can achieve an average Equal Error Rate (EER) of 4.93% of the skill forgery database.

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Published

2015-11-05

How to Cite

Huang, D., & Gao, J. (2015). On-line Signature Verification Based on GA-SVM. International Journal of Online and Biomedical Engineering (iJOE), 11(6), pp. 49–53. https://doi.org/10.3991/ijoe.v11i6.5122

Issue

Section

Papers