Abstract:
Signature recognition is an area which has been exposed to vast amount of
research works, with diversity of directions. Signature recognition can be divided into
two main areas depending on the data acquisition method: on-line and off-line
signature recognition. From the subject of machine recognition of handwritten
signature methods, the online recognition systems can acquire time dependant
information like writing acceleration, pressure and pen moment in addition to the
resulting signature. Therefore the online recognition systems give excellent
recognition results. But, timely dependant local feature details are not available in the
offline systems. Since offline signature recognition remains important in variety of
applications. The machine recognition of the signature is a very special and difficult
problem. The allied constraints arise due to the complexity of signature patterns, the
wide variations in the patterns of a single person (i.e. inter personnel variations) and
the forged signatures produced by the alleged forgers. The application areas for
signature recognition include all the applications where handwritten signatures are
already in use such as in the bank transactions, credit card operations or authentication
of a document with important instructions or information.
In this study, I proposed a 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 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. The recognition results authenticate that this is a reliable and
accurate system for off-line recognition of handwritten signatures.
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