Signature Forgery Verification

Abstract

This paper presents an innovative approach for signature verification and forgery detection based on fuzzy modelling. 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 (γ) 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.Signature
verification and forgeIy detection relate to the process of verifying signatures automatically and instantly to determine whether the signature
is genuine o r forged. There are two main types of signature verification: static and dynamic. Static, or off-line verification is the process
of verifying an electronic or paper signature after it has been made, while dynamic or on-line verification takes place as a subject creates his
signature on a digital tablet or a similar device.