Global Features for the Off-Line Signature Verification Problem
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Global features based on the boundary of a signature and its projections are described for enhancing the process of automated signature verification. The first global feature is derived from the total 'energy' a writer uses to create their signature. The second feature employs information from the vertical and horizontal projections of a signature, focusing on the proportion of the distance between key strokes in the image, and the height/width of the signature. The combination of these features with the Modified Direction Feature (MDF) and the ratio feature showed promising results for the off-line signature verification problem. When being trained using 12 genuine specimens and 400 random forgeries taken from a publicly available database, the Support Vector Machine (SVM) classifier obtained an average error rate (AER) of 17.25%. The false acceptance rate (FAR) for random forgeries was also kept as low as 0.08%.
Proceedings of the 10th International Conference on Document Analysis annd Recognition
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Pattern Recognition and Data Mining
Neural, Evolutionary and Fuzzy Computation