Signature verification and forgery detection system

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modeling. The signature images are binarized and resized to a fixed size window and are then thinned. The thinned image is then partitioned into a fixed number of eight sub-images called boxes. This partition is done using the horizontal density approximation approach. Each sub-image is then further resized and again partitioned into twelve further sub-images using the uniform partitioning approach. The features of consideration are normalized vector angle (a) and distance (/spl gamma/) from each box. Each feature extracted from sample signatures gives rise to fuzzy sets. Since the choice of a proper fuzzification function is crucial for verification, we have devised a new fuzzification function with structural parameters, which is able to adapt to the variations in fuzzy sets. This function is employed to develop a complete forgery detection and verification system.