Research Article Open Access

Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine

k. R. Radhika, M. K. Venkatesha and G. N. Sekhar

Abstract

Problem statement: The research addressed the computational load reduction in off-line signature verification based on minimal features using bayes classifier, fast Fourier transform, linear discriminant analysis, principal component analysis and support vector machine approaches. Approach: The variation of signature in genuine cases is studied extensively, to predict the set of quad tree components in a genuine sample for one person with minimum variance criteria. Using training samples, with a high degree of certainty the Minimum Variance Quad tree Components (MVQC) of a signature for a person are listed to apply on imposter sample. First, Hu moment is applied on the selected subsections. The summation values of the subsections are provided as feature to classifiers. Results: Results showed that the SVM classifier yielded the most promising 8% False Rejection Rate (FRR) and 10% False Acceptance Rate (FAR). The signature is a biometric, where variations in a genuine case, is a natural expectation. In the genuine signature, certain parts of signature vary from one instance to another. Conclusion: The proposed system aimed to provide simple, faster robust system using less number of features when compared to state of art works.

Journal of Computer Science
Volume 6 No. 3, 2010, 305-311

DOI: https://doi.org/10.3844/jcssp.2010.305.311

Submitted On: 8 March 2005 Published On: 31 March 2010

How to Cite: Radhika, K. R., Venkatesha, M. K. & Sekhar, G. N. (2010). Off-Line Signature Authentication Based on Moment Invariants Using Support Vector Machine. Journal of Computer Science, 6(3), 305-311. https://doi.org/10.3844/jcssp.2010.305.311

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Keywords

  • Off-line signature authentication
  • Hu moments
  • Quad tree decomposition
  • SVM classifier