An Evolving Signature Recognition System

This paper proposed a signature recognition method based on the fuzzy logic and genetic algorithm (GA) methodologies. It consists of two phases; the fuzzy inference system training using GA and the signature recognition. A sample of signatures is used to represent a particular person. The feature extraction process is followed by a selective preprocessing. The fuzzy inference system is followed by a feature extraction step. The projection profiles, contour profiles, geometric centre, actual dimensions, signature area, local features, and the baseline shift are considered as the feature set in the study. The input feature set is divided into five sections and separate five fuzzy subsystems were used to take the results. Those results are combined using a second stage fuzzy system. The fuzzy membership functions are optimized using the GA. A set of signatures consisting of genuine signatures, random forgeries, skilled forgeries of a particular signature and different signatures were used as the training set. Then, that particular optimized recognition system can be used to identify the particular signature identity. System achieved a signature recognition rate of about 90% and handled the random forgeries with 77 % accuracy and skilled forgeries with 70% accuracy. The recognition results authenticate that this is a reliable and accurate system for off-line recognition of handwritten signatures