The computerized verification of scanned, handwritten signatures
has been extensively studied in the past decades, but there are
still several possibilities for improvement in the field. To
achieve better verification results, we propose a simplified
probabilistic model for off-line signature verification. In our
model, each of the verification steps can be mathematically
described and, therefore, individually analyzed and improved.
Using this model, we are able to predict the accuracy of a
signature verification system based on just a few a priori known
parameters, such as the cardinality and the quality of input
samples. Several experiments have been conducted using our
statistics-based classifier to confirm the assumptions and the
results of our model. Based on the results, we can provide
answers to several old questions within the field, such as why
is it so hard to achieve error rates below 10% or how does the
number of original samples and features affect the final error
rates.
