Offline handwritten signature verification through network radial basis functions optimized by Differential Evolution

The handwritten signature is present in all important documents. In law, if the signature on a document is false, this document is also considered a fraud. This paper uses a neural network of radial basis function optimized by Differential Evolution Algorithm with features that best discriminates between a genuine signature of a simulated forgery. The experiments with this promising technique were made with a GPDS-300 gray images base and the results subjected to statistical tests with the performance of technical literature.