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.
Highlights
Hand written signature is widely accepted all around the world socially and legally
The design of a complete automatic handwritten signature verification system which is able to cope with all classes of forgeries is a very difficult task
The material is organized in following manner: In the Prologue, preliminaries which describes quad tree, Hu moments, variance and the classifiers are explained
Summary
Hand written signature is widely accepted all around the world socially and legally. There exist some challenging aspects in signature verification. Quad trees are classified according to the type of data they represent, including areas, points, lines and curves. Let R represent the entire normalized binary signature image. The proposed system implements first, second and third trie level of decomposition to represent variable size of the normalized binary signature.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have