EVOLUTIONARY-BASED APPROACH FOR SOLVING DIGITAL SIGNATURE RECOGNITION TASK

The work reported in the paper aims to develop an image registration methodology for digital signature recognition task. In our research we propose a solution to the problem of a particular component of banking security systems involving the client's signature. Any document reflecting legal transactions executed by a certain bank that includes client’s signature should be pre-processed by the security system in order to authorize it. One of the first steps in the authorization process involves the recognition of the client’s digital signature. Basically, the digital signature to be recognized is often different from the stored one from the geometrical point of view, in the sense that it could be a distorted variant of it. In our developments the working assumption is that the degradation is modeled by rigid transform, where only translation, rotation, and scaling are considered. The registration problem is solved based on the following general procedure. First, the binary variants of both the acquired image and the stored one are computed to make the entire recognition process tractable. Next an evolutionary-based technique is developed to align the input image to the target one. The proposed fitness function is defined in terms of mutual information computed between the transformed image and the target image. We use various mixtures of standard recombination schemes, involving local/global convex and discrete crossover. The mutation procedure comprises uncorrelated multiple sigma-type parameters. The registration quality is evaluated in the final phase using quantitative and qualitative measures. The experimental results together with some concluding remarks regarding the quality of the proposed methodology are reported in the final part of the paper.