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

Journal of Computer Science

G. N. Sekhar¹  k. R. Radhika¹  M. K. Venkatesha¹ 

关键词: Off-line signature authentication;    Hu moments;    Quad tree decomposition;    SVM classifier;   

DOI  :  10.3844/jcssp.2010.305.311

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

Unknown