Abstract
Signature verification is a technology that can solve security problems in our networked society. A new improved offline signature verification scheme is proposed which is based on selecting 60 feature points from the geometric centre of the signature and compares them with the already trained feature points. The classification of the feature points utilizes statistical parameters like mean and variance. The suggested scheme discriminates between two types of originals and forged signatures. The method takes care of skill, simple and random forgeries. The objective is to reduce the two vital parameters-False Acceptance Rate (FAR) and False Rejection Rate (FRR) which are normally used in any signature verification scheme. Comparative analysis has been made with standard existing schemes.
Highlights
Signature verification is an important research area in the field of personal authentication
We concern with offline signature verification which is based on geometric centre and is useful in separating skilled forgeries from the originals
Forgeries taken by three persons and 8 from each
Summary
Signature verification is an important research area in the field of personal authentication. We concern with offline signature verification which is based on geometric centre and is useful in separating skilled forgeries from the originals. Divide the image into two halves w.r.t. the number of black pixels by two lines vertically and horizontally which intersects at a point called the geometric centre.
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