  Biometric authentication systems that make use of signature verification
methods often render optimum performance only under limited and restricted
conditions. Such methods utilize several training samples so as to achieve high
accuracy. Moreover, several constraints are imposed on the end-user so that the
system may work optimally, and as expected. For example, the user is made to
sign within a small box, in order to limit their signature to a predefined set
of dimensions, thus eliminating scaling. Moreover, the angular rotation with
respect to the referenced signature that will be inadvertently introduced as
human error, hampers performance of biometric signature verification systems.
To eliminate this, traditionally, a user is asked to sign exactly on top of a
reference line. In this paper, we propose a robust system that optimizes the
signature obtained from the user for a large range of variation in
Rotation-Scaling-Translation (RST) and resolves these error parameters in the
user signature according to the reference signature stored in the database.
